CN117392561B

CN117392561B - Remote sensing unmanned aerial vehicle image processing method and system for intelligent traffic construction data acquisition

Info

Publication number: CN117392561B
Application number: CN202311287726.3A
Authority: CN
Inventors: 张蕴灵; 辛光涛; 王惠; 杨思宇; 宋红霞; 黄永杰
Original assignee: China Highway Engineering Consultants Corp; CHECC Data Co Ltd
Current assignee: China Highway Engineering Consultants Corp; CHECC Data Co Ltd
Priority date: 2023-10-07
Filing date: 2023-10-07
Publication date: 2024-05-14
Anticipated expiration: 2043-10-07
Also published as: CN117392561A

Abstract

The application relates to a remote sensing unmanned aerial vehicle image processing method and a system for intelligent traffic construction data acquisition, which relate to the technical field of traffic construction, and the method comprises the following steps: acquiring image information acquired by an unmanned aerial vehicle and construction requirement information; according to the construction requirement information, calling image requirement reference information corresponding to the construction requirement information; analyzing and processing the image demand reference information according to a preset image feature extraction method to form reference image feature information; analyzing and processing the collected image information and the reference image characteristic information of the unmanned aerial vehicle according to a preset image characteristic comparison method to form collected image comparison result information; analyzing and processing the acquired image comparison result information according to a preset image output analysis method to output comparison success image output information, and outputting the comparison success image output information. The application has the effect of facilitating the subsequent rapid analysis of the image acquired by the remote sensing unmanned aerial vehicle.

Description

Remote sensing unmanned aerial vehicle image processing method and system for intelligent traffic construction data acquisition

Technical Field

The application relates to the technical field of traffic construction, in particular to a remote sensing unmanned aerial vehicle image processing method and system for intelligent traffic construction data acquisition.

Background

Traffic construction refers to the activities of constructing, building, maintaining and managing traffic infrastructure such as roads, railways, pipelines, cableways, bridges, tunnels and the like. The intelligent traffic construction refers to the application of unmanned intelligent construction in traffic infrastructure construction engineering.

In the related technology, because unmanned aerial vehicle has advantages such as mobility, flexibility, accuracy, thoroughly and detailed measurement, so at present when carrying out intelligent traffic construction, generally adopt remote sensing unmanned aerial vehicle to measure the road, remote sensing unmanned aerial vehicle gathers a large amount of images to the road condition of different topography from each angle, and after the remote sensing unmanned aerial vehicle transmitted the image of gathering to processing system, processing system handled data and construction demand etc. that gathers, plan optimal scheme to the convenience is followed and is constructed.

With respect to the related art as described above, the applicant found the following drawbacks: when adopting remote sensing unmanned aerial vehicle in wisdom traffic work progress, generally gather the transmission to processing system through remote sensing unmanned aerial vehicle to the image, handle the image by processing system again, and the image quantity that remote sensing unmanned aerial vehicle gathered generally increases progressively along with the increase of time to lead to processing system to need carry out analysis to a large amount of images, and then inconvenient follow-up carry out quick analysis to the image that remote sensing unmanned aerial vehicle gathered.

Disclosure of Invention

In order to facilitate subsequent rapid analysis of images acquired by the remote sensing unmanned aerial vehicle, the application provides a remote sensing unmanned aerial vehicle image processing method and system for intelligent traffic construction data acquisition.

In a first aspect, the application provides a remote sensing unmanned aerial vehicle image processing method for intelligent traffic construction data acquisition, which adopts the following technical scheme:

the image processing method of the remote sensing unmanned aerial vehicle for intelligent traffic construction data acquisition comprises the following steps:

acquiring image information acquired by an unmanned aerial vehicle and construction requirement information;

according to the construction requirement information, calling image requirement reference information corresponding to the construction requirement information;

Analyzing and processing the image demand reference information according to a preset image feature extraction method to form reference image feature information;

analyzing and processing the collected image information and the reference image characteristic information of the unmanned aerial vehicle according to a preset image characteristic comparison method to form collected image comparison result information;

analyzing and processing the acquired image comparison result information according to a preset image output analysis method to output comparison success image output information, and outputting the comparison success image output information.

By adopting the technical scheme, the image information and the construction requirement information are acquired through the unmanned aerial vehicle, the construction requirement information is used for calling the image requirement reference information, the image requirement reference information is analyzed and processed through the image feature extraction method to form the reference image feature information, the unmanned aerial vehicle is acquired through the image feature comparison method to analyze and process the image information and the reference image feature information to form the acquired image comparison result information, the acquired image comparison result information is analyzed and processed through the image output analysis method to output and compare the successful image output information, and therefore the subsequent rapid analysis of the images acquired by the remote sensing unmanned aerial vehicle is facilitated.

Optionally, the analyzing the image demand reference information according to the preset image feature extraction method to form reference image feature information includes:

Retrieving image demand equipment type information and image demand environment information corresponding to the image demand reference information according to the image demand reference information;

Analyzing and acquiring equipment type characteristic information corresponding to the image demand equipment type information according to the corresponding relation between the image demand equipment type information and preset equipment type characteristic information;

according to the corresponding relation between the image demand environment information and the preset environment characteristic information, analyzing and acquiring the environment characteristic information corresponding to the image demand environment information;

And analyzing and acquiring the reference image comprehensive characteristic information corresponding to the equipment type characteristic information and the image type characteristic information according to the corresponding relation between the equipment type characteristic information and the image type characteristic information and the preset reference image comprehensive characteristic information, and taking the reference image comprehensive characteristic information as the reference image characteristic information.

Through adopting above-mentioned technical scheme, retrieve image demand equipment kind information and image demand environment information through image demand benchmark information, acquire equipment kind characteristic information through image demand equipment kind information analysis, acquire environment characteristic information through image demand environment information analysis, acquire benchmark image comprehensive characteristic information through equipment kind characteristic information and image kind characteristic information analysis, and regard benchmark image comprehensive characteristic information as benchmark image characteristic information, thereby improve the accuracy of benchmark image characteristic information who acquires.

Optionally, the analyzing the collected image information and the reference image feature information of the unmanned aerial vehicle according to the preset image feature comparison method to form collected image comparison result information includes:

Acquiring flight state information of the unmanned aerial vehicle;

According to a preset flight state influence analysis method, unmanned aerial vehicle flight state information is analyzed and processed to form flight state influence characteristic information;

Based on unmanned aerial vehicle collected image information, comparing the flight state influence characteristic information, deleting the image information consistent with the flight state influence characteristic information, and taking the unmanned aerial vehicle collected image information after deleting the image information as collected image influence adjustment information;

Analyzing and processing the reference image characteristic information and the collected image influence adjustment information according to a preset image characteristic similarity analysis method to form a collected image characteristic similarity;

Judging whether the feature similarity of the acquired image is smaller than the preset feature reference similarity;

if yes, outputting preset image non-similar result information and taking the image non-similar result information as acquired image comparison result information;

If not, analyzing and acquiring acquired image similar result information corresponding to the acquired image feature similarity and the unmanned aerial vehicle acquired image information according to the acquired image feature similarity and the corresponding relation between the unmanned aerial vehicle acquired image information and the preset acquired image similar result information, and taking the acquired image similar result information as acquired image comparison result information.

According to the technical scheme, the flight state information of the unmanned aerial vehicle is obtained, the flight state information of the unmanned aerial vehicle is analyzed and processed through the flight state influence analysis method to form flight state influence characteristic information, the unmanned aerial vehicle collected image information is compared and deleted to generate collected image influence adjustment information, the reference image characteristic information and the collected image influence adjustment information are analyzed and processed through the image characteristic similarity analysis method to form collected image characteristic similarity, whether the collected image characteristic similarity is smaller than a preset characteristic reference similarity or not is judged, when the collected image characteristic similarity is smaller than the preset characteristic reference similarity, the preset image non-similarity result information is output and is used as collected image comparison result information, when the collected image similarity is not smaller than the preset image non-similarity result information, the collected image similarity result information is obtained through collected image characteristic similarity and unmanned aerial vehicle collected image information analysis, and the collected image similarity result information is used as collected image comparison result information, and therefore the accuracy of the obtained collected image comparison result information is improved.

Optionally, the analyzing the flight status information of the unmanned aerial vehicle according to the preset flight status influence analysis method to form flight status influence feature information includes:

retrieving current weather condition information corresponding to the unmanned aerial vehicle flight state information and a current altitude value according to the unmanned aerial vehicle flight state information;

according to the corresponding relation between the current weather condition information and the preset current weather influence characteristic information, analyzing and acquiring the current weather influence characteristic information corresponding to the current weather condition information;

According to the corresponding relation between the current height value and the preset height influence characteristic information, analyzing and acquiring the height influence characteristic information corresponding to the current height value;

according to the corresponding relation between the current weather-influencing characteristic information, the height-influencing characteristic information and the preset unmanned aerial vehicle comprehensive-influencing characteristic information, analyzing and acquiring unmanned aerial vehicle comprehensive-influencing characteristic information corresponding to the current weather-influencing characteristic information and the height-influencing characteristic information, and taking the unmanned aerial vehicle comprehensive-influencing characteristic information as flight state influence characteristic information.

Through adopting above-mentioned technical scheme, transfer current weather condition information and current altitude value through unmanned aerial vehicle flight status information, acquire current weather effect characteristic information through current weather condition information analysis, acquire altitude effect characteristic information through current altitude value analysis, acquire unmanned aerial vehicle comprehensive influence characteristic information through current weather effect characteristic information and altitude effect characteristic information analysis to with unmanned aerial vehicle comprehensive influence characteristic information as flight status influence characteristic information, thereby improve the accuracy of the flight status influence characteristic information who acquires.

Optionally, the analyzing the reference image feature information and the collected image influence adjustment information according to the preset image feature similarity analysis method to form the collected image feature similarity includes:

Comparing the characteristic information of the reference image with the influence adjustment information of the acquired image, and analyzing and acquiring the similarity between the characteristic information of the reference image and the influence adjustment information of the acquired image to serve as the initial similarity of the characteristics;

judging whether the initial similarity of the features is larger than the preset reference similarity of the features;

if yes, taking the initial feature similarity as the feature similarity of the acquired image;

if not, comparing the acquired image influence adjustment information with the reference image characteristic information, analyzing and acquiring a similar region between the reference image characteristic information and the acquired image influence adjustment information to serve as actual similar region information of the acquired image, and taking similar reference image characteristic information as reference image similar characteristic information;

according to the corresponding relation between the actual similar region information of the acquired image and the similar feature information of the reference image and the preset similar feature residual region information, analyzing and acquiring similar feature residual region information corresponding to the actual similar region information of the acquired image and the similar feature information of the reference image;

Invoking a similar feature residual area value corresponding to the similar feature residual area information according to the similar feature residual area information;

According to the flight state influence characteristic information, acquiring a flight state influence characteristic area value corresponding to the flight state influence characteristic information;

According to the corresponding relation between the similar characteristic residual area value and the flight state influence characteristic area value and the preset similar characteristic residual area influence value, analyzing and obtaining similar characteristic residual area influence values corresponding to the similar characteristic residual area value and the flight state influence characteristic area value;

And analyzing and acquiring the characteristic influence adjustment similarity corresponding to the similar characteristic residual area influence value and the characteristic initial similarity according to the corresponding relation between the similar characteristic residual area influence value, the characteristic initial similarity and the preset characteristic influence adjustment similarity, and taking the characteristic influence adjustment similarity as the acquired image characteristic similarity.

By adopting the technical scheme, the similarity between the reference image characteristic information and the acquired image influence adjustment information is obtained through analysis and is used as the characteristic initial similarity, whether the characteristic initial similarity is larger than the preset characteristic reference similarity is judged, when the characteristic initial similarity is larger than the preset characteristic reference similarity, the characteristic initial similarity is used as the acquired image characteristic similarity, when the characteristic initial similarity is not larger than the preset characteristic reference similarity, the acquired image actual similarity region information and the reference image similarity characteristic information are defined, the acquired image actual similarity region information and the reference image similarity characteristic information are analyzed to obtain the similar characteristic residual region information, the similar characteristic residual region area value is retrieved through the similar characteristic residual region information, the flight state influence characteristic area value is retrieved through the flight state influence characteristic information, the similar characteristic residual region influence value is obtained through analysis of the similar characteristic residual region influence value and the characteristic initial similarity, and the characteristic influence adjustment similarity is used as the acquired image characteristic similarity, and therefore the accuracy of the acquired image characteristic similarity is improved.

Optionally, the analyzing the acquired image comparison result information according to the preset image output analysis method to output comparison success image output information includes:

judging whether the acquired image comparison result information is preset image non-similar result information;

If yes, continuing to acquire the image information acquired by the unmanned aerial vehicle;

If not, the acquired image comparison similar characteristic information corresponding to the acquired image comparison result information is called according to the acquired image comparison result information;

According to the acquired image comparison similar characteristic information, similar characteristic type information corresponding to the acquired image comparison similar characteristic information is acquired;

And according to a preset characteristic type output analysis method, analyzing and processing similar characteristic type information and acquired image comparison result information to form characteristic type output information, and taking the characteristic type output information as comparison success image output information.

By adopting the technical scheme, whether the acquired image comparison result information is the preset image non-similar result information is judged, when the acquired image comparison result information is not the preset image non-similar result information, the unmanned aerial vehicle acquired image information is continuously acquired, when the acquired image comparison result information is not the preset image non-similar result information, the acquired image comparison result information is called, the similar characteristic type information is called through the acquired image comparison result information, the similar characteristic type information and the acquired image comparison result information are analyzed and processed through the characteristic type output analysis method, the characteristic type output information is taken as comparison success image output information, and therefore the accuracy of the acquired comparison success image output information is improved.

Optionally, the outputting the analysis method according to the preset feature type to analyze the similar feature type information and the acquired image comparison result information to form feature type output information includes:

judging whether the similar characteristic type information is only one;

if so, analyzing and acquiring single-type output information corresponding to the similar characteristic type information and the acquired image comparison result information according to the corresponding relation between the similar characteristic type information, the acquired image comparison result information and the preset single-type output information, and taking the single-type output information as the characteristic type output information;

If not, analyzing and processing the similar characteristic type information according to a preset single type construction type analysis method to form single type construction type information;

analyzing and processing similar characteristic type information and single type construction type information according to a preset construction type composite analysis method to form construction type composite information;

and analyzing and acquiring composite type construction output information corresponding to the construction type composite information and the acquired image comparison result information according to the corresponding relation between the construction type composite information, the acquired image comparison result information and the preset composite type construction output information, and taking the composite type construction output information as characteristic type output information.

By adopting the technical scheme, whether the similar characteristic type information is only one is judged, when the similar characteristic type information is only one, single type output information is obtained through analysis of the similar characteristic type information and the acquired image comparison result information, the single type output information is used as the characteristic type output information, when the similar characteristic type information is not only one, the single type construction type information is formed through analysis processing of the similar characteristic type information by the single type construction type analysis method, the similar characteristic type information and the single type construction type information are analyzed and processed by the construction type composite analysis method, construction type composite information is formed, composite type construction output information is obtained through analysis of the construction type composite information and the acquired image comparison result information, and the composite type construction output information is used as the characteristic type output information, so that the accuracy of the obtained characteristic type output information is improved.

Optionally, the analyzing the similar characteristic category information according to the preset single category construction category analysis method to form single category construction category information includes:

Selecting one of the similar feature type information at will and using the selected information as single similar feature type information, and using the rest similar feature type information as rest similar feature type information;

invoking single-type initial construction category information corresponding to the single-type information of the similar features according to the single-type information of the similar features;

Retrieving initial category number values corresponding to the single category initial category information according to the single category initial category information;

retrieving the number of the similar feature types corresponding to the similar feature type information according to the similar feature type information;

analyzing and acquiring a category number reference value corresponding to the category number value of the similar feature according to the corresponding relation between the category number value of the similar feature and a preset category number reference value;

judging whether the number of the initial category categories is larger than a category number reference value or not;

If yes, the single type of initial construction category information is used as single type of construction category information;

if not, one of the similar feature type information is selected again and used as similar feature single type information, and the similar feature type information is remained and used as similar feature residual type information.

By adopting the technical proposal, the single type information of the similar characteristics and the residual type information of the similar characteristics are selected randomly, the single type initial construction type information is called through the single type information of the similar characteristics, the number of the initial type is called through the single type initial type information, the number of the type is obtained through the number analysis of the similar characteristics, and judging whether the number of the initial category types is larger than the category number reference value, when the number of the initial category types is larger than the category number reference value, taking the single-category initial construction category information as single-category construction category information, and when the number of the initial category types is not larger than the category number reference value, reselecting one of the similar characteristic category information and taking the same as similar characteristic single-category information, and taking the rest of the similar characteristic category information as similar characteristic rest category information, thereby improving the accuracy of the acquired single-category construction category information.

Optionally, the analyzing the similar characteristic type information and the single type construction type information according to the preset construction type composite analysis method to form construction type composite information includes:

According to the construction category information of the single category, the construction requirement characteristic category information of the single category corresponding to the construction category information of the single category is called;

Judging whether the single type construction requirement feature type information is consistent with the similar feature residual type information or not;

if yes, the type information which is consistent with the rest type information of the similar characteristics in the single type construction requirement characteristic type information is taken as the characteristic consistent type information;

analyzing and acquiring feature distinguishing type information corresponding to the similar feature residual type information and the feature consistent type information according to the corresponding relation between the similar feature residual type information, the feature consistent type information and the preset feature distinguishing type information;

analyzing and acquiring feature type composite information corresponding to the feature consistent type information and the feature distinguishing type information according to the corresponding relation between the feature consistent type information, the feature distinguishing type information and the preset feature type composite information, and taking the feature type composite information as construction type composite information;

If not, the jump execution re-selects one of the similar feature type information as similar feature single type information, and the similar feature type information remains as similar feature remaining type information.

By adopting the technical scheme, the single type construction requirement characteristic type information is obtained through the single type construction type information, whether the single type construction requirement characteristic type information is consistent with the similar characteristic residual type information or not is judged, when the single type construction requirement characteristic type information is consistent with the similar characteristic residual type information, the type information consistent with the similar characteristic residual type information in the single type construction requirement characteristic type information is used as the characteristic consistent type information, the characteristic distinguishing type information is obtained through the analysis of the similar characteristic residual type information and the characteristic consistent type information, the characteristic type composite information is obtained through the analysis of the characteristic consistent type information and the characteristic distinguishing type information, the characteristic type composite information is used as the construction type composite information, when the single type construction requirement characteristic type information is not present, the jump execution is carried out to reselect one of the similar characteristic type information and is used as the similar characteristic single type information, and the similar characteristic type information is remained as the similar characteristic residual type information, so that the accuracy of the obtained construction type composite information is improved.

In a second aspect, the application provides a remote sensing unmanned aerial vehicle image processing system for intelligent traffic construction data acquisition, which adopts the following technical scheme:

Wisdom traffic construction data gathers with remote sensing unmanned aerial vehicle image processing system, include:

The acquisition module is used for acquiring image information acquired by the unmanned aerial vehicle, construction requirement information and flight state information of the unmanned aerial vehicle;

A memory for storing a program of the remote sensing unmanned aerial vehicle image processing method for intelligent traffic construction data collection according to any one of the first aspect;

The processor, the program in the memory can be loaded and executed by the processor, and the remote sensing unmanned aerial vehicle image processing method for intelligent traffic construction data acquisition according to any one of the first aspect is realized.

Through adopting above-mentioned technical scheme, acquire unmanned aerial vehicle collection image information, construction demand information and unmanned aerial vehicle flight status information through the acquisition module to program loading execution in the memory is carried out through the treater, thereby makes things convenient for follow-up quick analysis to the image that remote sensing unmanned aerial vehicle gathered.

In summary, the present application includes at least one of the following beneficial technical effects:

1. Acquiring image information and construction requirement information pairs through an unmanned aerial vehicle, calling image requirement reference information through construction requirement information, analyzing and processing the image requirement reference information through an image feature extraction method to form reference image feature information, analyzing and processing the image information acquired by the unmanned aerial vehicle and the reference image feature information through an image feature comparison method to form acquired image comparison result information, analyzing and processing the acquired image comparison result information through an image output analysis method to output comparison success image output information and output the comparison success image output information, and therefore convenience is brought to follow-up rapid analysis of images acquired by a remote sensing unmanned aerial vehicle;

2. The method comprises the steps of retrieving image demand equipment type information and image demand environment information through image demand reference information, analyzing and obtaining equipment type feature information through the image demand equipment type information, analyzing and obtaining environment feature information through the image demand environment information, analyzing and obtaining reference image comprehensive feature information through the equipment type feature information and the image type feature information, and taking the reference image comprehensive feature information as reference image feature information, so that the accuracy of the obtained reference image feature information is improved;

3. The unmanned aerial vehicle flight state information is obtained, the unmanned aerial vehicle flight state information is analyzed and processed through a flight state influence analysis method to form flight state influence characteristic information, the unmanned aerial vehicle acquisition image information is used for comparing and deleting the flight state influence characteristic information and generating acquisition image influence adjustment information, the reference image characteristic information and the acquisition image influence adjustment information are analyzed and processed through an image characteristic similarity analysis method to form acquisition image characteristic similarity, whether the acquisition image characteristic similarity is smaller than a preset characteristic reference similarity or not is judged, when the acquisition image characteristic similarity is smaller than the preset characteristic reference similarity, the preset image non-similarity result information is output and is used as acquisition image comparison result information, when the acquisition image similarity is not smaller than the preset characteristic reference similarity, the acquisition image similarity result information is obtained through acquisition image characteristic similarity and unmanned aerial vehicle acquisition image information analysis, and the acquired image similarity result information is used as acquisition image comparison result information, so that the accuracy of the acquired acquisition image comparison result information is improved.

Drawings

Fig. 1 is a flowchart of a method for processing an image of a remote sensing unmanned aerial vehicle for intelligent traffic construction data acquisition according to an embodiment of the present application.

Fig. 2 is a flowchart of a method for analyzing image demand reference information to form reference image feature information according to a preset image feature extraction method according to an embodiment of the present application.

Fig. 3 is a flowchart of a method for analyzing and processing image information collected by an unmanned aerial vehicle and reference image feature information according to a preset image feature comparison method to form collected image comparison result information according to an embodiment of the present application.

Fig. 4 is a flowchart of a method for analyzing and processing unmanned aerial vehicle flight status information to form flight status influence characteristic information according to a preset flight status influence analysis method according to an embodiment of the application.

Fig. 5 is a flowchart of a method for analyzing and processing reference image feature information and collected image influence adjustment information to form a collected image feature similarity according to a preset image feature similarity analysis method according to an embodiment of the present application.

Fig. 6 is a flowchart of a method for analyzing acquired image comparison result information to output comparison success image output information according to a preset image output analysis method according to an embodiment of the present application.

Fig. 7 is a flowchart of a method for analyzing similar feature type information and collected image comparison result information according to a preset feature type output analysis method to form feature type output information according to an embodiment of the present application.

Fig. 8 is a flowchart of a method for analyzing similar feature type information to form single type construction type information according to a preset single type construction type analysis method according to an embodiment of the present application.

Fig. 9 is a flowchart of a method for analyzing similar feature type information and single type construction type information according to a preset construction type composite analysis method to form construction type composite information according to an embodiment of the present application.

Fig. 10 is a flowchart of a system for processing an image of a remote sensing unmanned aerial vehicle for intelligent traffic construction data acquisition according to an embodiment of the present application.

Reference numerals illustrate: 1. an acquisition module; 2. a memory; 3. a processor.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings 1 to 10 and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.

Referring to fig. 1, the embodiment of the application discloses a remote sensing unmanned aerial vehicle image processing method for intelligent traffic construction data acquisition, which comprises the following steps:

Step S100: and acquiring image information and construction requirement information acquired by the unmanned aerial vehicle.

Wherein, unmanned aerial vehicle gathers image information and refers to the image information that the remote sensing unmanned aerial vehicle that gathers the data when carrying out wisdom traffic construction gathered image information and gathers through remote sensing unmanned aerial vehicle and acquire, and construction demand information refers to current demand information when carrying out wisdom traffic construction, and construction demand information is through operator's input in advance and is obtained.

Step S200: and calling the image demand reference information corresponding to the construction demand information according to the construction demand information.

The image demand reference information refers to reference image information currently required during intelligent traffic construction, and the image demand reference information is inquired and obtained from a database storing the image demand reference information. The image requirement reference information is called, so that the subsequent use is convenient.

Step S300: and analyzing and processing the image demand reference information according to a preset image characteristic extraction method to form reference image characteristic information.

The reference image feature information refers to reference feature information in a reference image which is currently required, the image feature extraction method refers to a method for extracting features in the image, and the image feature extraction method is obtained by inquiring a database storing the image feature extraction method.

The image demand reference information is analyzed and processed through the image feature extraction method, so that reference image feature information is formed, and the reference image feature information is convenient to use subsequently.

Step S400: according to a preset image feature comparison method, analyzing and processing the image information collected by the unmanned aerial vehicle and the reference image feature information to form collected image comparison result information.

The image feature comparison method is used for carrying out feature comparison on the image, and is obtained by inquiring a database storing the image feature comparison method.

The unmanned aerial vehicle acquired image information and the reference image characteristic information are analyzed and processed through the image characteristic comparison method, so that acquired image comparison result information is formed, and the unmanned aerial vehicle is convenient to use subsequently.

Step S500: analyzing and processing the acquired image comparison result information according to a preset image output analysis method to output comparison success image output information, and outputting the comparison success image output information.

The image output analysis method is used for carrying out output analysis on the image, and is obtained by inquiring a database storing the image output analysis method. The comparison success image output information refers to output information for outputting a comparison success image.

The acquired image comparison result information is analyzed and processed through the image output analysis method, so that comparison success image output information is output, and comparison success image output information is output, so that the images acquired by the remote sensing unmanned aerial vehicle are subjected to pre-feature comparison processing, the images successfully compared are output, and further the subsequent rapid analysis of the images acquired by the remote sensing unmanned aerial vehicle is facilitated.

In step S300 shown in fig. 1, in order to further secure the rationality of the reference image feature information, further individual analysis calculation of the reference image feature information is required, and specifically, the detailed description will be given by the steps shown in fig. 2.

Referring to fig. 2, the analysis processing of the image demand reference information to form reference image feature information according to a preset image feature extraction method includes the steps of:

Step S310: and retrieving the type information of the image demand equipment and the image demand environment information corresponding to the image demand reference information according to the image demand reference information.

The image demand equipment type information refers to type information of current demand equipment when intelligent traffic construction is carried out, and the image demand equipment type information is inquired and obtained from a database storing the image demand equipment type information. The image demand environment information refers to environment information for indicating a current demand at the time of intelligent traffic construction, and is obtained by querying from a database storing the image demand environment information.

The type information of the image demand equipment and the image demand environment information are called through the image demand reference information, so that the type information of the image demand equipment and the image demand environment information can be conveniently acquired subsequently.

Step S320: and analyzing and acquiring the equipment type characteristic information corresponding to the image demand equipment type information according to the corresponding relation between the image demand equipment type information and the preset equipment type characteristic information.

The device type characteristic information is characteristic information for indicating the current required device, and is obtained by inquiring from a database storing the device type characteristic information.

The equipment type characteristic information is obtained through analysis of the image demand equipment type information, so that the equipment type characteristic information is convenient to use subsequently.

Step S330: according to the corresponding relation between the image demand environment information and the preset environment characteristic information, analyzing and acquiring the environment characteristic information corresponding to the image demand environment information.

The environment characteristic information refers to characteristic information of the current demand environment, and the environment characteristic information is obtained by inquiring from a database storing the environment characteristic information.

Environmental characteristic information is obtained through analysis of the image demand environmental information, so that the image demand environmental information can be conveniently used later.

Step S340: and analyzing and acquiring the reference image comprehensive characteristic information corresponding to the equipment type characteristic information and the image type characteristic information according to the corresponding relation between the equipment type characteristic information and the image type characteristic information and the preset reference image comprehensive characteristic information, and taking the reference image comprehensive characteristic information as the reference image characteristic information.

The reference image comprehensive characteristic information refers to comprehensive characteristic information in the reference image, and the reference image comprehensive characteristic information is inquired and acquired from a database storing the reference image comprehensive characteristic information.

And analyzing and acquiring the reference image comprehensive characteristic information through the equipment type characteristic information and the image type characteristic information, and taking the reference image comprehensive characteristic information as the reference image characteristic information, so that the acquired reference image characteristic information is influenced by the equipment type and the environment, and the accuracy of the acquired reference image characteristic information is further improved.

In step S400 shown in fig. 1, in order to further ensure the rationality of the acquired image comparison result information, further individual analysis and calculation of the acquired image comparison result information is required, specifically, the detailed description is given by the steps shown in fig. 3.

Referring to fig. 3, according to a preset image feature comparison method, the method for analyzing the collected image information and the reference image feature information of the unmanned aerial vehicle to form collected image comparison result information includes the following steps:

Step S410: and acquiring flight state information of the unmanned aerial vehicle.

The unmanned aerial vehicle flight state information is used for indicating flight state information of the remote sensing unmanned aerial vehicle when data are acquired, and the unmanned aerial vehicle flight state information is detected and acquired through a sensor preset on the remote sensing unmanned aerial vehicle.

Step S420: according to a preset flight state influence analysis method, the unmanned aerial vehicle flight state information is analyzed and processed to form flight state influence characteristic information.

The flight state influence characteristic information refers to influence characteristic information generated by the flight state of the remote sensing unmanned aerial vehicle when data are collected, and the flight state influence analysis method refers to an analysis method for analyzing influence generated by the flight state of the remote sensing unmanned aerial vehicle when the data are collected, wherein the flight state influence analysis method is obtained by inquiring a database storing the flight state influence analysis method.

The flight state influence analysis method is used for analyzing and processing the flight state information of the unmanned aerial vehicle, so that flight state influence characteristic information is formed, and the subsequent use of the flight state influence characteristic information is facilitated.

Step S430: and acquiring image information based on the unmanned aerial vehicle to compare the flight state influence characteristic information, deleting the image information consistent with the flight state influence characteristic information, and taking the unmanned aerial vehicle acquired image information after deleting the image information as acquired image influence adjustment information.

The unmanned aerial vehicle image information is compared with the flight state influence characteristic information, the image information consistent with the flight state influence characteristic information is deleted from the unmanned aerial vehicle image information, and the unmanned aerial vehicle image information after the image information is deleted is used as the image information adjustment information, so that the subsequent use of the image information adjustment information is facilitated.

Step S440: and analyzing and processing the reference image characteristic information and the collected image influence adjustment information according to a preset image characteristic similarity analysis method to form the collected image characteristic similarity.

The image feature similarity is used for similarity when features in the acquired image are similar to features in the reference image, the image feature similarity analysis method is used for analyzing the feature similarity in the image, and the image feature similarity analysis method is obtained by inquiring a database storing the image feature similarity analysis method.

And analyzing and processing the reference image characteristic information and the collected image influence adjustment information by an image characteristic similarity analysis method, so that the collected image characteristic similarity is formed, and the subsequent use is convenient.

Step S450: judging whether the feature similarity of the acquired image is smaller than the preset feature reference similarity. If yes, go to step S460; if not, step S470 is performed.

The feature reference similarity refers to reference similarity when features are similar, and the feature reference similarity is obtained by inquiring a database storing the feature reference similarity.

And judging whether the acquired image feature similarity is smaller than a preset feature reference similarity or not, so as to judge whether the acquired image meets the current required requirement or not.

Step S460: and outputting preset image non-similar result information and taking the same as acquired image comparison result information.

The image dissimilar result information is result information used for indicating that the images are dissimilar, and the image dissimilar result information is obtained by inquiring a database storing the image dissimilar result information.

When the feature similarity of the acquired image is smaller than the preset feature reference similarity, the condition that the acquired image does not meet the current required requirement is indicated, so that the preset image non-similarity result information is output and used as the acquired image comparison result information, and the accuracy of the acquired image comparison result information is improved.

Step S470: according to the feature similarity of the acquired image, the corresponding relation between the acquired image information of the unmanned aerial vehicle and the preset acquired image similar result information, analyzing and acquiring the acquired image similar result information corresponding to the feature similarity of the acquired image and the acquired image information of the unmanned aerial vehicle, and taking the acquired image similar result information as acquired image comparison result information.

The acquired image similar result information is the result information used for indicating that the acquired images are similar, and the acquired image similar result information is inquired and acquired from a database storing the acquired image similar result information.

When the feature similarity of the acquired image is not less than the preset feature reference similarity, the acquired image meets the current required requirement, so that the acquired image similarity result information is acquired through analysis of the feature similarity of the acquired image and the acquired image information of the unmanned aerial vehicle, and the acquired image similarity result information is used as acquired image comparison result information, and the accuracy of the acquired image comparison result information is improved.

In step S420 shown in fig. 3, in order to further ensure the rationality of the flight status affecting feature information, further individual analysis calculation of the flight status affecting feature information is required, specifically, the detailed description will be given by the steps shown in fig. 4.

Referring to fig. 4, according to a preset flight status influence analysis method, the method for analyzing and processing the flight status information of the unmanned aerial vehicle to form flight status influence characteristic information includes the following steps:

Step S421: and retrieving current weather condition information and a current altitude value corresponding to the unmanned aerial vehicle flight state information according to the unmanned aerial vehicle flight state information.

The unmanned aerial vehicle flight state information comprises current weather condition information and a current altitude value, wherein the current weather condition information refers to weather condition information of a position where the remote sensing unmanned aerial vehicle is located at the current time, and the current weather condition information is inquired and obtained from a database storing the current weather condition information. The current altitude value refers to the altitude value of the position of the remote sensing unmanned aerial vehicle at the current time, and the current altitude value is obtained by inquiring a database storing the current altitude value.

The current weather condition information and the current altitude value are called through the unmanned aerial vehicle flight state information, so that the current weather condition information and the current altitude value are convenient to use in the follow-up process.

Step S422: and analyzing and acquiring the current weather effect characteristic information corresponding to the current weather condition information according to the corresponding relation between the current weather condition information and the preset current weather effect characteristic information.

The current weather-influencing characteristic information refers to influence characteristic information generated by weather at the current time, and is obtained by inquiring a database storing the current weather-influencing characteristic information.

And the current weather effect characteristic information is obtained through analysis of the current weather condition information, so that the current weather effect characteristic information is convenient to use in the follow-up process.

Step S423: and analyzing and acquiring the height influence characteristic information corresponding to the current height value according to the corresponding relation between the current height value and the preset height influence characteristic information.

The height influence characteristic information refers to influence characteristic information generated by the height of the position where the remote sensing unmanned aerial vehicle is located at the current time, and the height influence characteristic information is inquired and obtained from a database storing the height influence characteristic information.

And acquiring the height influence characteristic information through analysis of the current height value, so that the subsequent use of the height influence characteristic information is facilitated.

Step S424: according to the corresponding relation between the current weather-influencing characteristic information, the height-influencing characteristic information and the preset unmanned aerial vehicle comprehensive-influencing characteristic information, analyzing and acquiring unmanned aerial vehicle comprehensive-influencing characteristic information corresponding to the current weather-influencing characteristic information and the height-influencing characteristic information, and taking the unmanned aerial vehicle comprehensive-influencing characteristic information as flight state influence characteristic information.

The unmanned aerial vehicle comprehensive influence characteristic information refers to comprehensive influence characteristic information generated when the remote sensing unmanned aerial vehicle collects data, and the unmanned aerial vehicle comprehensive influence characteristic information is inquired and obtained from a database storing the unmanned aerial vehicle comprehensive influence characteristic information.

The comprehensive influence characteristic information of the unmanned aerial vehicle is obtained through the analysis of the current weather influence characteristic information and the altitude influence characteristic information, and is used as the flight state influence characteristic information, so that the obtained flight state influence characteristic information is influenced by weather and altitude, and the accuracy of the obtained flight state influence characteristic information is improved.

In step S440 shown in fig. 3, in order to further ensure the rationality of the feature similarity of the acquired image, further separate analysis and calculation of the feature similarity of the acquired image is required, specifically, the detailed description will be given by the steps shown in fig. 5.

Referring to fig. 5, according to a preset image feature similarity analysis method, the analysis processing of the reference image feature information and the collected image influence adjustment information to form a collected image feature similarity includes the following steps:

step S441: and comparing the acquired image influence adjustment information with the reference image characteristic information, and analyzing and acquiring the similarity between the reference image characteristic information and the acquired image influence adjustment information to serve as the characteristic initial similarity.

The feature initial similarity refers to initial similarity when similarity exists in the features, and the reference image feature information and the collected image influence adjustment information are compared, so that the similarity between the reference image feature information and the collected image influence adjustment information is analyzed, obtained and used as the feature initial similarity, and the feature initial similarity is convenient to use subsequently.

Step S442: and judging whether the initial similarity of the features is larger than the preset reference similarity of the features. If yes, go to step S443; if not, step S444 is performed.

And judging whether the initial similarity of the features is larger than the preset feature reference similarity or not, so as to judge whether the features meet the similar reference requirement or not when the features are similar.

Step S443: and taking the initial feature similarity as the feature similarity of the acquired image.

When the initial feature similarity is larger than the preset feature reference similarity, the feature similarity meets the similar reference requirement when the features are similar, so that the initial feature similarity is used as the acquired image feature similarity, and the accuracy of the acquired image feature similarity is improved.

Step S444: comparing the acquired image influence adjustment information with the reference image characteristic information, analyzing and acquiring a similar area between the reference image characteristic information and the acquired image influence adjustment information to serve as actual similar area information of the acquired image, and taking similar reference image characteristic information as reference image similar characteristic information.

The acquired image actual similarity region information refers to region information corresponding to actual similarity when similarity exists in the acquired image, and the reference image similarity feature information refers to reference feature information corresponding to similarity when similarity exists in the acquired image.

When the initial similarity of the features is not greater than the preset reference similarity of the features, the condition that the features are similar at the moment does not meet the similar reference requirement is indicated, so that the similar region between the reference image feature information and the collected image influence adjustment information is analyzed and obtained and used as the actual similar region information of the collected image by comparing the reference image feature information with the collected image influence adjustment information, and the similar reference image feature information is used as the reference image similar feature information, so that the actual similar region information of the collected image and the reference image similar feature information are conveniently used later.

Step S445: and analyzing and acquiring similar feature residual area information corresponding to the actual similar area information of the acquired image and the similar feature information of the reference image according to the corresponding relation between the actual similar area information of the acquired image and the similar feature information of the reference image and the preset similar feature residual area information.

The similar feature residual area information refers to area information which is remained except actual similarity when the acquired image features are similar in the reference features, and the similar feature residual area information is obtained by inquiring a database storing the similar feature residual area information.

And acquiring the actual similar region information of the image and the similar feature information of the reference image by analyzing to acquire the residual region information of the similar feature, thereby facilitating the subsequent use of the residual region information of the similar feature.

Step S446: and retrieving the area value of the similar feature residual area corresponding to the similar feature residual area information according to the similar feature residual area information.

The area value of the residual area of the similar feature refers to the area value corresponding to the residual area when the similar feature exists, and the area value of the residual area of the similar feature is obtained by inquiring a database storing the area value of the residual area of the similar feature.

And the area value of the similar feature residual area is fetched through the similar feature residual area information, so that the subsequent use of the similar feature residual area value is convenient.

Step S447: and calling the flight state influence characteristic area value corresponding to the flight state influence characteristic information according to the flight state influence characteristic information.

The flying state influence characteristic area value refers to an area value corresponding to the flying state generation influence characteristic of the remote sensing unmanned aerial vehicle, and the flying state influence characteristic area value is obtained by inquiring a database storing the flying state influence characteristic area value.

The flight state influence characteristic area value is retrieved through the flight state influence characteristic information, so that the subsequent use of the flight state influence characteristic area value is facilitated.

Step S448: and analyzing and acquiring the similar characteristic residual area influence value corresponding to the similar characteristic residual area value and the flight state influence characteristic area value according to the corresponding relation between the similar characteristic residual area value and the flight state influence characteristic area value and the preset similar characteristic residual area influence value.

The similar feature residual area influence value refers to an influence degree value when the residual area is influenced, and the similar feature residual area influence value is obtained by inquiring a database storing the similar feature residual area influence value.

And analyzing and acquiring the area value of the similar characteristic residual area and the area value of the flight state influence characteristic area to obtain the influence value of the similar characteristic residual area, thereby facilitating the subsequent use.

Step S449: and analyzing and acquiring the characteristic influence adjustment similarity corresponding to the similar characteristic residual area influence value and the characteristic initial similarity according to the corresponding relation between the similar characteristic residual area influence value, the characteristic initial similarity and the preset characteristic influence adjustment similarity, and taking the characteristic influence adjustment similarity as the acquired image characteristic similarity.

The feature influence adjustment similarity refers to similarity obtained by adjusting the similarity when the rest area is influenced, and the feature influence adjustment similarity is obtained by inquiring a database storing the feature influence adjustment similarity.

And obtaining feature influence adjustment similarity through analysis of the influence value of the residual region of the similar feature and the initial similarity of the feature, and taking the feature influence adjustment similarity as the acquired image feature similarity, so that when the features are similar but the similar reference requirement is not met, the influence value of the residual region of the similar feature is obtained through analysis, thereby reducing the influence of the flight state on the feature information, and further improving the accuracy of the acquired image feature similarity.

In step S500 shown in fig. 1, in order to further ensure the rationality of comparing the successful image output information, further individual analysis calculation of the comparison successful image output information is required, specifically, the steps shown in fig. 6 are described in detail.

Referring to fig. 6, according to a preset image output analysis method, the analysis processing of the acquired image comparison result information to output comparison success image output information includes the following steps:

step S510: judging whether the acquired image comparison result information is the preset image non-similar result information. If yes, go to step S520; if not, step S530 is performed.

And judging whether the acquired image comparison result information is the preset image non-similar result information or not, so as to judge whether further processing is needed when the acquired image comparison result information is output.

Step S520: and continuously acquiring image information acquired by the unmanned aerial vehicle.

When the acquired image comparison result information is the preset image and no similar result information exists, the acquired image information of the unmanned aerial vehicle is continuously acquired because no further processing is needed when the acquired image comparison result information is output.

Step S530: and calling the acquired image comparison similar characteristic information corresponding to the acquired image comparison result information according to the acquired image comparison result information.

The acquired image comparison similar characteristic information refers to similar characteristic information when the acquired images are similar after being compared, and the acquired image comparison similar characteristic information is obtained by inquiring a database storing the acquired image comparison similar characteristic information.

When the acquired image comparison result information is not the preset image and has no similar result information, the acquired image comparison result information needs to be further processed when the acquired image comparison result information is output, so that the acquired image comparison similar characteristic information is called through the acquired image comparison result information, and the acquired image comparison similar characteristic information is conveniently used later.

Step S540: and invoking similar characteristic type information corresponding to the acquired image comparison similar characteristic information according to the acquired image comparison similar characteristic information.

The similar feature type information refers to type information corresponding to similar features, and the similar feature type information is obtained by inquiring a database storing the similar feature type information.

And the similar characteristic type information is acquired by comparing the acquired images with the similar characteristic information, so that the subsequent use of the similar characteristic type information is facilitated.

Step S550: and according to a preset characteristic type output analysis method, analyzing and processing similar characteristic type information and acquired image comparison result information to form characteristic type output information, and taking the characteristic type output information as comparison success image output information.

The feature type output information refers to output information for outputting according to a feature type, and the feature type output analysis method refers to an analysis method for analyzing the feature type output information, wherein the feature type output analysis method is obtained by inquiring a database storing the feature type output analysis method.

The similar characteristic type information and the acquired image comparison result information are analyzed and processed through the characteristic type output analysis method, so that characteristic type output information is formed, the characteristic type output information is used as comparison success image output information, the acquired comparison success image output information is affected by the similar characteristic type information, and the accuracy of the acquired comparison success image output information is improved.

In step S550 shown in fig. 6, in order to further secure the rationality of the feature type output information, further individual analysis calculation of the feature type output information is required, and specifically, the detailed description will be given by the steps shown in fig. 7.

Referring to fig. 7, the method for analyzing similar feature type information and acquired image comparison result information according to a preset feature type output analysis method to form feature type output information includes the following steps:

Step S551: it is determined whether the similar feature class information is only one. If yes, go to step S552; if not, step S553 is performed.

And judging whether the output information needs to be further processed or not by judging whether the similar characteristic type information is only one.

Step S552: according to the corresponding relation between the similar characteristic type information, the acquired image comparison result information and the preset single type output information, analyzing and acquiring the single type output information corresponding to the similar characteristic type information and the acquired image comparison result information, and taking the single type output information as the characteristic type output information.

The single-type output information refers to output information for instructing to perform single-type output, and is obtained by searching a database storing single-type output information.

When the similar characteristic type information is only one, the fact that the output information does not need to be further processed is indicated, so that single type of output information is obtained through analysis of the similar characteristic type information and the acquired image comparison result information, the single type of output information is used as the characteristic type output information, and the accuracy of the obtained characteristic type output information is improved.

Step S553: according to the preset single-type construction category analysis method, similar characteristic category information is analyzed and processed to form single-type construction category information.

The single-type construction category information refers to construction category information corresponding to characteristics indicating a single type, the single-type construction category analysis method refers to an analysis method for analyzing the single-type construction category information, and the single-type construction category analysis method is obtained by inquiring a database storing the single-type construction category analysis method.

When the number of the similar characteristic type information is not only one, the output information is required to be further processed, so that the similar characteristic type information is analyzed and processed through a single type construction type analysis method, and single type construction type information is formed, and the follow-up use is convenient.

Step S554: and analyzing and processing the similar characteristic type information and the single type construction type information according to a preset construction type composite analysis method to form construction type composite information.

The construction category type composite information refers to information obtained by compositing categories according to construction categories, the construction category type composite analysis method refers to an analysis method for analyzing the construction category type composite information, and the construction category type composite analysis method is obtained by inquiring a database storing the construction category type composite analysis method.

The construction category type composite analysis method is used for analyzing and processing similar characteristic type information and single type construction category information, so that construction category type composite information is formed, and the construction category type composite information is convenient to use subsequently.

Step S555: and analyzing and acquiring composite type construction output information corresponding to the construction type composite information and the acquired image comparison result information according to the corresponding relation between the construction type composite information, the acquired image comparison result information and the preset composite type construction output information, and taking the composite type construction output information as characteristic type output information.

The composite type construction output information refers to output information output according to the type of construction to be composited, and the composite type construction output information is obtained by inquiring a database storing the composite type construction output information.

The composite type construction output information is obtained through analysis of the construction type composite information and the acquired image comparison result information, and the composite type construction output information is used as the characteristic type output information, so that the obtained characteristic type output information is influenced by the construction type composite information, and the accuracy of the obtained characteristic type output information is improved.

In step S553 shown in fig. 7, in order to further secure the rationality of the single-type construction type information, further individual analysis and calculation of the single-type construction type information is required, and specifically, the detailed description will be given by the steps shown in fig. 8.

Referring to fig. 8, the analysis processing of similar feature category information to form single category construction category information according to a preset single category construction category analysis method includes the steps of:

Step S5531: one of the similar feature type information is arbitrarily selected and used as similar feature single type information, and the similar feature type information is used as similar feature residual type information.

The similar feature single category information refers to category information indicating a single category in the similar feature, and the similar feature residual category information refers to other residual category information except the similar feature single category information in the similar feature category information.

And the method and the device have the advantages that one of the similar characteristic type information is arbitrarily selected and used as similar characteristic single type information, and the rest of the similar characteristic type information is used as similar characteristic rest type information, so that the follow-up use of the similar characteristic single type information and the similar characteristic rest type information is facilitated.

Step S5532: and calling single-type initial construction category information corresponding to the single-type information of the similar features according to the single-type information of the similar features.

The single-type initial construction category information refers to initial category information for indicating a construction category corresponding to a single type, and the single-type initial construction category information is obtained by inquiring a database storing the single-type initial construction category information.

The single type of initial construction category information is acquired through the single type of information with similar characteristics, so that the single type of initial construction category information is convenient to use subsequently.

Step S5533: and retrieving the initial category number value corresponding to the single category initial category information according to the single category initial category information.

The initial category number value refers to a category number value of a category included in the initial category, and the initial category number value is obtained by querying a database storing the initial category number value.

The number of the initial category types is called through the single initial category information, so that the subsequent use of the number of the initial category types is convenient.

Step S5534: and retrieving the number of the similar feature types corresponding to the similar feature type information according to the similar feature type information.

The number of similar feature types refers to the number of all types of similar features, and the number of similar feature types is obtained by inquiring a database storing the number of similar feature types.

The number of the similar feature types is called through the similar feature type information, so that the subsequent use of the number of the similar feature types is convenient.

Step S5535: and analyzing and acquiring the category number reference value corresponding to the similar feature category number value according to the corresponding relation between the similar feature category number value and the preset category number reference value.

The category number reference value refers to a reference number value of the number of categories corresponding to the tolerable construction category among all categories in the similar feature, and the category number reference value is obtained by querying from a database storing the category number reference value.

The category number reference value is obtained through the category number value analysis of the similar characteristic, so that the category number reference value is convenient to use subsequently.

Step S5536: and judging whether the number of the initial category categories is larger than a category number reference value. If yes, go to step S5537; if not, step S5538 is performed.

And judging whether the number of the initial type is larger than the number reference value of the type, so as to judge whether the single type of initial construction type information meets the requirement.

Step S5537: and taking the single type of initial construction category information as single type of construction category information.

When the number of the initial category is larger than the standard value of the category number, the single-category initial construction category information meets the requirement, so that the single-category initial construction category information is used as the single-category construction category information.

Step S5538: and re-selecting one of the similar feature type information as similar feature single type information, and remaining similar feature type information as similar feature remaining type information.

When the number of the initial category types is not larger than the category number reference value, the condition that the single category initial construction category information does not meet the requirement is indicated, so that one of the similar characteristic category information is selected again and used as the similar characteristic single category information, and the rest of the similar characteristic category information is used as the similar characteristic rest category information, and the accuracy of the obtained single category construction category information is improved.

In step S554 shown in fig. 7, in order to further secure the rationality of the construction type composite information, further separate analysis and calculation of the construction type composite information is required, and specifically, the detailed description will be given by the steps shown in fig. 9.

Referring to fig. 9, the method for analyzing similar characteristic category information and single category construction category information according to a preset construction category composite analysis method to form construction category composite information includes the steps of:

Step S5541: and retrieving the single-type construction requirement characteristic type information corresponding to the single-type construction type information according to the single-type construction type information.

The construction requirement characteristic type information of a single type refers to type information contained in a construction type corresponding to the characteristic of the single type, and the construction requirement characteristic type information of the single type is inquired and obtained from a database storing the construction requirement characteristic type information of the single type.

The single type construction requirement characteristic type information is retrieved through the single type construction type information, so that the follow-up use of the single type construction requirement characteristic type information is facilitated.

Step S5542: and judging whether the single type of construction requirement feature type information is consistent with the similar feature residual type information. If yes, go to step S5543; if not, step S5546 is performed.

And judging whether the characteristics in the same construction requirement can be output simultaneously by judging whether the characteristics in the single construction requirement are consistent with the characteristics in the similar residual types of information.

Step S5543: and taking the type information which is consistent with the rest type information of the similar characteristics in the single type construction requirement characteristic type information as the characteristic consistent type information.

The feature matching type information refers to type information when features match. When the single type construction requirement feature type information is consistent with the similar feature residual type information, the feature in the same construction requirement can be output at the same time, so that the type information consistent with the similar feature residual type information in the single type construction requirement feature type information is used as feature consistent type information, and the follow-up use of the feature consistent type information is facilitated.

Step S5544: and analyzing and acquiring the characteristic distinguishing type information corresponding to the similar characteristic residual type information and the characteristic consistent type information according to the corresponding relation between the similar characteristic residual type information, the characteristic consistent type information and the preset characteristic distinguishing type information.

The characteristic distinguishing type information refers to other type information except for the characteristic consistent type information in the similar characteristic residual type information, and the characteristic distinguishing type information is obtained by inquiring a database storing the characteristic distinguishing type information.

And analyzing and acquiring the characteristic distinguishing type information through the residual type information of the similar characteristic and the characteristic consistent type information, so that the characteristic distinguishing type information is convenient to use subsequently.

Step S5545: according to the corresponding relation between the feature consistent type information, the feature distinguishing type information and the preset feature type composite information, analyzing and obtaining feature type composite information corresponding to the feature consistent type information and the feature distinguishing type information, and taking the feature type composite information as construction type composite information.

The feature type composite information is type information indicating that types of features are combined, and the feature type composite information is obtained by querying a database storing the feature type composite information.

And analyzing and acquiring feature type composite information through feature consistent type information and feature distinguishing type information, and taking the feature type composite information as construction type composite information, so that the accuracy of the acquired construction type composite information is improved.

Step S5546: the jump proceeds to step S5538.

When there is no match between the single type of construction requirement feature type information and the similar feature remaining type information, it is indicated that the features in the same construction requirement cannot be output at the same time, so step S5538 is skipped.

Referring to fig. 10, based on the same inventive concept, an embodiment of the present invention provides a remote sensing unmanned aerial vehicle image processing system for intelligent traffic construction data acquisition, including:

the acquisition module 1 is used for acquiring image information acquired by the unmanned aerial vehicle, construction requirement information and flight state information of the unmanned aerial vehicle;

A memory 2 for storing a program of a remote sensing unmanned aerial vehicle image processing method for intelligent traffic construction data collection as described in any one of fig. 1 to 9;

the processor 3, the program in the memory can be loaded and executed by the processor and implement the remote sensing unmanned aerial vehicle image processing method for intelligent traffic construction data acquisition as described in any one of fig. 1 to 9.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional modules is illustrated, and in practical application, the above-described functional allocation may be performed by different functional modules according to needs, i.e. the internal structure of the apparatus is divided into different functional modules to perform all or part of the functions described above. The specific working processes of the above-described systems, devices and units may refer to the corresponding processes in the foregoing method embodiments, which are not described herein.

The foregoing description of the preferred embodiments of the application is not intended to limit the scope of the application in any way, including the abstract and drawings, in which case any feature disclosed in this specification (including abstract and drawings) may be replaced by alternative features serving the same, equivalent purpose, unless expressly stated otherwise. That is, each feature is one example only of a generic series of equivalent or similar features, unless expressly stated otherwise.

Claims

1. The image processing method of the remote sensing unmanned aerial vehicle for intelligent traffic construction data acquisition is characterized by comprising the following steps of:

analyzing the acquired image comparison result information according to a preset image output analysis method to output comparison success image output information, and outputting comparison success image output information;

the step of analyzing the image demand reference information according to the preset image feature extraction method to form reference image feature information comprises the following steps:

analyzing and acquiring reference image comprehensive characteristic information corresponding to the equipment type characteristic information and the image type characteristic information according to the corresponding relation between the equipment type characteristic information, the image type characteristic information and the preset reference image comprehensive characteristic information, and taking the reference image comprehensive characteristic information as the reference image characteristic information;

according to a preset image feature comparison method, analyzing and processing the collected image information and the reference image feature information of the unmanned aerial vehicle to form collected image comparison result information comprises the following steps:

Acquiring flight state information of the unmanned aerial vehicle;

If not, analyzing and acquiring acquired image similar result information corresponding to the acquired image feature similarity and the unmanned aerial vehicle acquired image information according to the acquired image feature similarity and the corresponding relation between the unmanned aerial vehicle acquired image information and the preset acquired image similar result information, and taking the acquired image similar result information as acquired image comparison result information;

According to a preset image feature similarity analysis method, the step of analyzing the reference image feature information and the collected image influence adjustment information to form the collected image feature similarity comprises the following steps:

2. The image processing method of a remote sensing unmanned aerial vehicle for intelligent traffic construction data acquisition according to claim 1, wherein the analyzing the unmanned aerial vehicle flight status information according to the preset flight status influence analysis method to form flight status influence characteristic information comprises:

3. The image processing method of a remote sensing unmanned aerial vehicle for intelligent traffic construction data acquisition according to claim 1, wherein the analyzing the acquired image comparison result information according to the preset image output analysis method to output comparison success image output information comprises:

4. The image processing method of a remote sensing unmanned aerial vehicle for intelligent traffic construction data acquisition according to claim 3, wherein outputting an analysis method according to a preset feature type to analyze similar feature type information and acquired image comparison result information to form feature type output information comprises:

judging whether the similar characteristic type information is only one;

5. The image processing method of a remote sensing unmanned aerial vehicle for intelligent traffic construction data collection according to claim 4, wherein the analyzing the similar characteristic type information according to the preset single type construction type analysis method to form single type construction type information comprises:

6. The image processing method of a remote sensing unmanned aerial vehicle for intelligent traffic construction data collection according to claim 5, wherein the analyzing the similar characteristic type information and the single type construction type information according to the preset construction type composite analysis method to form construction type composite information comprises:

7. Wisdom traffic construction data gathers with remote sensing unmanned aerial vehicle image processing system, its characterized in that includes:

The acquisition module (1) is used for acquiring image information acquired by the unmanned aerial vehicle, construction requirement information and flight state information of the unmanned aerial vehicle;

A memory (2) for storing a program of the remote sensing unmanned aerial vehicle image processing method for intelligent traffic construction data collection according to any one of claims 1 to 6;

A processor (3), a program in the memory can be loaded and executed by the processor and implement the remote sensing unmanned aerial vehicle image processing method for intelligent traffic construction data acquisition according to any one of claims 1 to 6.