CN117496449A - Method and related device for detecting atmospheric pollutants in construction site based on image analysis - Google Patents
Method and related device for detecting atmospheric pollutants in construction site based on image analysis Download PDFInfo
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Abstract
The application relates to the technical field of image processing and provides a method and a related device for detecting atmospheric pollutants in a construction site based on image analysis, wherein the method comprises the steps of acquiring image information of a detected area in real time and judging whether the atmospheric pollutants and mechanical equipment exist in the detected area based on the image information; when the atmospheric pollutants and mechanical equipment exist in the detected area, carrying out association processing on the atmospheric pollutant information and the mechanical type information to obtain atmospheric pollutant-mechanical association information; acquiring pollutant concentration grade information of atmospheric pollutants based on the image information; generating an atmospheric contaminant detection report of the detected area based on the atmospheric contaminant-mechanical association information and the contaminant concentration level information, and sending the atmospheric contaminant detection report to a management platform; and generating alarm information based on the atmospheric pollutant detection report, and broadcasting the alarm information to staff in the detected area. The method improves the detection precision of the atmospheric pollutants on the construction site.
Description
Technical Field
The application relates to the technical field of image processing, in particular to a method and a related device for detecting atmospheric pollutants in a construction site based on image analysis.
Background
With the continuous prominence of environmental problems and air quality problems, more and more people begin to realize the influence of site atmosphere pollutants (such as black smoke and dust) on the health and environment of people. The treatment of the atmospheric pollutants on the construction site has become a common concern for the whole society, and relates to the problems of health and environmental protection of people. In order to improve the efficiency and accuracy of treatment of atmospheric pollutants on a construction site, the prior art mainly comprises two methods of manual inspection and an online dust monitoring system.
Manual inspection: the traditional method for detecting the atmospheric pollutants in the construction site requires personnel to carry out regular inspection, and then carry out rectification after finding out problems. The method has the defects of incomplete inspection, manual misjudgment and the like, so that the problem of atmospheric pollutants in the construction site cannot be effectively solved in time.
On-line dust monitoring system: the existing online dust monitoring system mainly collects data through a mounting sensor, and then uploads the data to a cloud for processing and monitoring. However, due to the problems of high equipment cost, complex deployment, delay in data processing, long distance between the equipment construction point and the atmospheric pollutants in the construction site, the application of the equipment in some construction site fields is still limited.
Disclosure of Invention
The application provides a method and a related device for detecting atmospheric pollutants on a construction site based on image analysis, so as to solve the problems set forth in the background technology.
In a first aspect, the present application provides a method for detecting atmospheric pollutants at a worksite based on image analysis, the method comprising:
acquiring image information of a detected area in real time through a preset camera device, and inputting the image information into a preset atmospheric pollutant detection model to obtain atmospheric pollutant information in the detected area;
judging whether the atmospheric pollutants exist in the detected area or not based on the atmospheric pollutant information;
if the detected area contains atmospheric pollutants, inputting the image information into a preset mechanical type detection model to obtain mechanical type information in the detected area;
judging whether mechanical equipment exists in the detected area or not based on the mechanical type information;
if mechanical equipment exists in the detected area, carrying out association processing on the atmospheric pollutant information and the mechanical type information to obtain atmospheric pollutant-mechanical association information;
acquiring pollutant concentration level information of the atmospheric pollutants based on the image information;
Generating an atmospheric contaminant detection report of the detected area based on the atmospheric contaminant-mechanical association information and the contaminant concentration level information, and transmitting the atmospheric contaminant detection report to a management platform;
and generating alarm information based on the atmospheric pollutant detection report, and broadcasting the alarm information to staff in the detected area.
In a second aspect, the present application provides a worksite atmospheric contaminant detection device based on image analysis, comprising:
the first acquisition module is used for acquiring image information of a detected area in real time through a preset camera device, inputting the image information into a preset atmospheric pollutant detection model and obtaining atmospheric pollutant information in the detected area;
the first judging module is used for judging whether the atmospheric pollutants exist in the detected area or not based on the atmospheric pollutant information;
the input module is used for inputting the image information into a preset mechanical type detection model to obtain mechanical type information in the detected area if the detected area contains atmospheric pollutants;
the second judging module is used for judging whether mechanical equipment exists in the detected area or not based on the mechanical type information;
The processing module is used for carrying out association processing on the atmospheric pollutant information and the mechanical type information if mechanical equipment exists in the detected area, so as to obtain atmospheric pollutant-mechanical association information;
a second acquisition module for acquiring contaminant concentration level information of the atmospheric contaminant based on the image information;
the first generation module is used for generating an atmospheric pollutant detection report of the detected area based on the atmospheric pollutant-mechanical association information and the pollutant concentration level information and sending the atmospheric pollutant detection report to a management platform;
and the second generation module is used for generating alarm information based on the atmospheric pollutant detection report and broadcasting the alarm information to staff in the detected area.
In a third aspect, the application provides a terminal device, the terminal device comprising a processor, a memory and a computer program stored on the memory and executable by the processor, wherein the computer program, when executed by the processor, implements the method for detecting atmospheric pollutants on a worksite based on image analysis.
In a fourth aspect, the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements a method for site atmospheric contaminant detection based on image analysis as described above.
The application provides a method and a related device for detecting atmospheric pollutants in a construction site based on image analysis, wherein the method comprises the following steps: acquiring image information of a detected area in real time through a preset camera device, and inputting the image information into a preset atmospheric pollutant detection model to obtain atmospheric pollutant information in the detected area; judging whether the atmospheric pollutants exist in the detected area or not based on the atmospheric pollutant information; if the detected area contains atmospheric pollutants, inputting the image information into a preset mechanical type detection model to obtain mechanical type information in the detected area; judging whether mechanical equipment exists in the detected area or not based on the mechanical type information; if mechanical equipment exists in the detected area, carrying out association processing on the atmospheric pollutant information and the mechanical type information to obtain atmospheric pollutant-mechanical association information; acquiring pollutant concentration level information of the atmospheric pollutants based on the image information; generating an atmospheric contaminant detection report of the detected area based on the atmospheric contaminant-mechanical association information and the contaminant concentration level information, and transmitting the atmospheric contaminant detection report to a management platform;
And generating alarm information based on the atmospheric pollutant detection report, and broadcasting the alarm information to staff in the detected area. According to the method, on one hand, the image information in the detected area is acquired in real time, the image information is intelligently analyzed based on an atmospheric pollutant detection model, a mechanical type detection model and an intelligent algorithm, so that a pollutant detection report is acquired, the pollutant detection report is sent to a management platform, the pollutant in the detected area is detected in real time, timely, accurate and comprehensive data support is provided for the management platform, and meanwhile, an atmospheric pollutant detection sensor does not need to be installed in the detected area, so that the economic cost is reduced, on the other hand, the alarm information is generated based on the atmospheric pollutant detection report, and the alarm information is broadcast to staff in the detected area, so that potential risks in the detected area can be reduced or even avoided, the monitoring efficiency of the detected area is improved, the capability of handling emergency events is improved, on the other hand, the method realizes comprehensive automatic detection of the atmospheric pollutant in the detected area, saves manpower, and improves the timeliness and the accuracy of the detection method.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a method for detecting atmospheric pollutants in a construction site based on image analysis according to an embodiment of the present application;
FIG. 2 is a schematic block diagram of a construction site atmospheric pollutant detection device based on image analysis according to an embodiment of the present application;
fig. 3 is a schematic block diagram of a structure of a terminal device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The flow diagrams depicted in the figures are merely illustrative and not necessarily all of the elements and operations/steps are included or performed in the order described. For example, some operations/steps may be further divided, combined, or partially combined, so that the order of actual execution may be changed according to actual situations.
It is also to be understood that the terminology used in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
With the continuous prominence of environmental problems and air quality problems, more and more people begin to realize the influence of site atmosphere pollutants (such as black smoke and dust) on the health and environment of people. The treatment of the atmospheric pollutants on the construction site has become a common concern for the whole society, and relates to the problems of health and environmental protection of people. In order to improve the efficiency and accuracy of treatment of atmospheric pollutants on a construction site, the prior art mainly comprises two methods of manual inspection and an online dust monitoring system.
Manual inspection: the traditional method for detecting the atmospheric pollutants in the construction site requires personnel to carry out regular inspection, and then carry out rectification after finding out problems. The method has the defects of incomplete inspection, manual misjudgment and the like, so that the problem of atmospheric pollutants in the construction site cannot be effectively solved in time.
On-line dust monitoring system: the existing online dust monitoring system mainly collects data through a mounting sensor, and then uploads the data to a cloud for processing and monitoring. However, due to the problems of high equipment cost, complex deployment, delay in data processing, long distance between the equipment construction point and the atmospheric pollutants in the construction site, the application of the equipment in some construction site fields is still limited. For this reason, the present application provides a method for detecting atmospheric pollutants at a worksite based on image analysis, so as to solve the above-mentioned problems.
Some embodiments of the present application are described in detail below with reference to the accompanying drawings. The following embodiments and features of the embodiments may be combined with each other without conflict.
Referring to fig. 1, fig. 1 is a flow chart of a method for detecting atmospheric pollutants at a worksite based on image analysis according to an embodiment of the present application, and as shown in fig. 1, the method for detecting atmospheric pollutants at a worksite based on image analysis according to an embodiment of the present application includes steps S100 to S800.
Step S100, acquiring image information of a detected area in real time through a preset image pickup device, and inputting the image information into a preset atmospheric pollutant detection model to obtain the atmospheric pollutant information in the detected area.
It should be noted that the air pollutants according to the embodiments of the present application include, but are not limited to, black smoke and dust.
The number of the imaging devices is not limited, and the imaging device may be at least one, so long as the whole image information of the detected region can be obtained.
The atmospheric pollutant detection model is obtained based on a YoloV5 network model training, and comprises an input layer, an image feature extraction layer, an image feature deep learning layer and an output layer, wherein the input layer is used for receiving the image information, the image feature extraction layer is used for extracting image features of the image information, the image feature deep learning layer is used for carrying out deep learning on the image features to obtain atmospheric pollutant information in the image information, and the output layer is used for outputting the atmospheric pollutant information. The training method for the atmospheric contaminant detection model is described in detail below, and is not described here.
And step 200, judging whether the atmospheric pollutants exist in the detected area based on the atmospheric pollutant information.
It can be understood that if no atmospheric contaminant exists in the detected area, the atmospheric contaminant information is information about the absence of atmospheric contaminant in the detected area, and if atmospheric contaminant exists in the detected area, the atmospheric contaminant information is information about the presence of atmospheric contaminant in the detected area, so the specific implementation method of step S200 determines whether the atmospheric contaminant exists in the detected area based on the specific content of the atmospheric contaminant information.
And step S300, if the detected area contains atmospheric pollutants, inputting the image information into a preset mechanical type detection model to obtain mechanical type information in the detected area.
The mechanical type detection model is obtained based on a YoloV5 network model training, and comprises an input layer, an image feature extraction layer, an image feature deep learning layer and an output layer, wherein the input layer is used for receiving the image information, the image feature extraction layer is used for extracting image features of the image information, the image feature deep learning layer is used for carrying out deep learning on the image features to obtain mechanical type information in the image information, and the output layer is used for outputting the mechanical type information.
And step 400, judging whether mechanical equipment exists in the detected area based on the mechanical type information.
It may be understood that, if no mechanical device exists in the detected area, the mechanical type information is information about the absence of mechanical devices in the detected area, and if the mechanical device exists in the detected area, the mechanical type information is information about the presence of mechanical devices in the detected area, so the specific implementation method of step S400 determines whether the mechanical device exists in the detected area based on specific content of the mechanical type information.
And step S500, if mechanical equipment exists in the detected area, carrying out association processing on the atmospheric pollutant information and the mechanical type information to obtain atmospheric pollutant-mechanical association information.
The atmospheric contaminant information includes an atmospheric contaminant type in the detected area and first area coverage information of atmospheric contaminants corresponding to the atmospheric contaminant types in the detected area, the machine type information includes a machine type in the detected area and second area coverage information of mechanical devices corresponding to the machine types in the detected area, wherein each atmospheric contaminant type corresponds to at least one first area coverage information, each machine type corresponds to at least one second area coverage information, and the correlation processing is performed on the atmospheric contaminant information and the machine type information, and the method includes the following steps:
Judging whether target second area coverage information corresponding to the first area coverage information exists in the second area coverage information according to the first area coverage information; wherein the mechanical equipment corresponding to the target second area coverage information and the atmospheric pollutants corresponding to the first area coverage information have overlapping coverage areas in the detected area;
for each piece of first area coverage information, if target second area coverage information corresponding to the first area coverage information exists in each piece of second area coverage information, determining first target associated mechanical equipment of the atmospheric pollutants corresponding to the first area coverage information based on the first area coverage information and each piece of target second area coverage information, and associating the atmospheric pollutants corresponding to the first area coverage information with the first target associated mechanical equipment; wherein the first target associated mechanical device is the mechanical device having a largest overlapping coverage area of the atmospheric contaminants within the detected area corresponding to the first area coverage information;
For each piece of first area coverage information, if target second area coverage information corresponding to the first area coverage information does not exist in each piece of second area coverage information, determining second target associated mechanical equipment based on the first area coverage information and each piece of target second area coverage information, calculating distance values between first designated position information of the second target associated mechanical equipment and each piece of second designated position information of a coverage area of the atmospheric pollutants in the detected area corresponding to the first area coverage information respectively, comparing each distance value with a preset distance value respectively, and associating the atmospheric pollutants corresponding to the first area coverage information with the second target associated mechanical equipment if any distance value is smaller than the preset distance value; the second target related mechanical equipment is the mechanical equipment above the center of the coverage area of the atmospheric pollutants in the detected area corresponding to the coverage information of the first area.
The first area coverage information comprises position information, contour information and area information of a coverage area of the atmospheric pollutants in the detected area, which correspond to the first area coverage information, and the second area coverage information comprises position information, contour information and area information of a coverage area of the mechanical equipment in the detected area, which correspond to the second area coverage information.
In the method for determining whether or not the target second area coverage information corresponding to the first area coverage information exists in each of the second area coverage information, the first area coverage information is compared with each of the second area coverage information to determine whether or not the target second area coverage information corresponding to the first area coverage information exists in each of the second area coverage information.
The method for determining the first target related mechanical device of the atmospheric contaminant corresponding to the first region coverage information based on the first region coverage information and the target second region coverage information is to analyze and compare the first region coverage information with the target second region coverage information to determine the first target related mechanical device.
The first specific location information may be any location information on the second target associated mechanical device, and the specific location information of the first specific location information is not limited, so long as the first specific location information is uniform for any second target associated mechanical device, for example, for any second target associated mechanical device, the first specific location information is the center of gravity of the second target associated mechanical device, for any second target associated mechanical device, the first specific location information is the bottom center location information of the second target key mechanical device, for example, for any second target associated mechanical device, the first specific location information is the top center location information of the second target associated mechanical device.
The second designated position information at least comprises a top surface center point of an circumscribed rectangle of a coverage area of the atmospheric pollutants in the detected area and center points of all side surfaces, wherein the top surface center point and the center points correspond to the coverage information of the first area.
The center of the second target related mechanical device above the center of the coverage area of the atmospheric contaminant in the detected area corresponding to the first area coverage information refers to the center of the top surface of the circumscribed rectangle of the coverage area of the atmospheric contaminant in the detected area corresponding to the first area coverage information, and all or part of the downward projection of the mechanical device falls on the top surface of the circumscribed rectangle.
It can be appreciated that, by adopting the method for performing the association processing on the atmospheric contaminant information and the mechanical type information by using the method, the first target association mechanical device or the second target association mechanical device can be accurately matched with the atmospheric contaminant corresponding to each first area coverage information, so that the accuracy of the association processing method is improved, the accuracy of the association method is improved, and the accuracy of an atmospheric contaminant detection report and alarm information is improved.
And step S600, acquiring pollutant concentration level information of the atmospheric pollutants based on the image information.
It should be noted that, the implementation manner of step S600 includes the following steps:
a Gaussian mixture model is adopted to construct a background removal model, and the image information is input into the background removal model to remove the image information irrelevant to the atmospheric pollutants in the image information, so as to obtain the image information of the atmospheric pollutants;
and for each first area coverage information, if the atmospheric pollutants corresponding to the first area coverage information exist in the first target related mechanical equipment or the second target related mechanical equipment, acquiring an average gray value of an image of a coverage area of the atmospheric pollutants corresponding to the first area coverage information in the detected area based on the atmospheric pollutant image information, and determining a pollutant concentration level of the atmospheric pollutants corresponding to the first area coverage information based on the average gray value.
Wherein the average gradation value is an average value of a sum of gradation values of all pixels of an image of a coverage area of the atmospheric contaminant within the detected area corresponding to the first area coverage information.
It will be appreciated that the atmospheric contaminant image information includes a plurality of atmospheric contaminant information blocks, each atmospheric contaminant information block representing image information of an atmospheric contaminant region from which profile information of the atmospheric contaminant region can be obtained.
When the average gray value of the image of the coverage area of the atmospheric contaminant in the detected area corresponding to the first area coverage information is obtained based on the atmospheric contaminant image information, an atmospheric contaminant information block matched with the first area coverage information is first determined in the atmospheric contaminant image information based on the contour information in the first area coverage information, then the gray value of each pixel in the atmospheric contaminant information block is calculated, and finally the average gray value of the gray value of each pixel is calculated.
The method for determining the pollutant concentration level of the atmospheric pollutant corresponding to the first area coverage information based on the average gray value is implemented by the following rule:
when 0.6< (H-m+L)/(H) is less than or equal to 0.8, the pollutant concentration grade is high;
When 0.4< (H-m+L)/(H) is less than or equal to 0.6, the pollutant concentration grade is medium grade;
when 0< (H-m+L)/(H is less than or equal to 0.4), the pollutant concentration grade is low;
wherein H and L are arbitrary values, m is the average gray value, H is greater than L, and the value of H-m+L is greater than 0.
And step S700, generating an atmospheric pollutant detection report of the detected area based on the atmospheric pollutant-mechanical association information and the pollutant concentration level information, and sending the atmospheric pollutant detection report to a management platform.
The atmospheric pollutant detection report comprises a pollutant area, mechanical equipment corresponding to the pollutant area and a pollution level corresponding to the pollutant area.
And step S800, generating alarm information based on the atmospheric pollutant detection report, and broadcasting the alarm information to staff in the detected area.
When the alarm information is generated based on the atmospheric contaminant detection report, the alarm information is generated to the atmospheric contaminant area corresponding to the intermediate contaminant level or the atmospheric contaminant area corresponding to the high contaminant level aiming at the intermediate contaminant level or the high contaminant level in the contaminant detection report, so that the staff in the relevant area can perform rectification in time.
According to the method provided by the embodiment, on one hand, the image information in the detected area is acquired in real time, the image information is intelligently analyzed based on the atmospheric pollutant detection model, the mechanical type detection model and the intelligent algorithm, so that the pollutant detection report is acquired, and is sent to the management platform, the pollutant in the detected area is detected in real time, timely, accurate and comprehensive data support is provided for the management platform, and meanwhile, an atmospheric pollutant detection sensor is not required to be installed in the detected area, so that the economic cost is reduced, on the other hand, the alarm information is generated based on the atmospheric pollutant detection report, and the alarm information is broadcast to staff in the detected area, so that potential risks in the detected area can be reduced or even avoided, the supervision efficiency of the detected area is improved, the capability of handling sudden events is improved, on the other hand, the method realizes comprehensive automatic detection of the atmospheric pollutant in the detected area, saves manpower, and improves the timeliness and accuracy of the detection method.
In some embodiments, the method of training the atmospheric contaminant detection model comprises the steps of:
acquiring a training sample set; the training sample set comprises a plurality of mapping relations, wherein the mapping relations are mapping relations between training image information and actual atmospheric pollutant information corresponding to the training image information, the training image information is image information acquired for the detected area, and the actual atmospheric pollutant information comprises actual atmospheric pollutant types in the training image information and actual area coverage information of atmospheric pollutants corresponding to the actual atmospheric pollutant types in the detected area;
dividing the training sample set into a training set and a correction set;
constructing a YoloV5 network model by adopting a YoloV5 algorithm, and training the YoloV5 network model based on the training set to obtain an initial atmospheric pollutant detection model;
respectively inputting the training image information in the correction set into the initial atmospheric pollutant detection model to obtain predicted atmospheric pollutant information of the training image information;
constructing a mapping relation table of predicted atmospheric pollutant information and actual atmospheric pollutant information based on the predicted atmospheric pollutant information and the actual atmospheric pollutant information corresponding to the training image information in the correction set;
Acquiring a thinking correction algorithm of the initial atmospheric pollutant detection model based on the mapping relation table of the predicted atmospheric pollutant information and the actual atmospheric pollutant information;
calculating an optimal correction factor of the initial atmospheric contaminant detection model based on the thought correction algorithm;
and adjusting model parameters of the initial atmospheric pollutant detection model based on the optimal correction factors to obtain the atmospheric pollutant detection model.
The actual area coverage information comprises position information, contour information and area information of an actual coverage area of the atmospheric pollutants in the detected area, wherein the actual coverage area information comprises the position information, the contour information and the area information of the actual coverage area of the atmospheric pollutants in the detected area, and the position information, the contour information and the area information correspond to the actual atmospheric pollutant type.
It is understood that the method of obtaining the thinking correction algorithm of the initial atmospheric contaminant detection model based on the predicted atmospheric contaminant information-actual atmospheric contaminant information map is to obtain difference information between the actual atmospheric contaminant information and the predicted atmospheric contaminant information by comparing the predicted atmospheric contaminant information and the actual atmospheric contaminant information corresponding to each of the training image information, and to generate the thinking correction algorithm based on each of the difference information. The optimal correction factor is used to improve the accuracy of the initial atmospheric contaminant detection model.
According to the training method for the atmospheric pollutant detection model, the thinking correction algorithm of the initial atmospheric pollutant detection model is obtained based on the predicted atmospheric pollutant information-actual atmospheric pollutant information mapping relation table, the optimal correction factor of the initial atmospheric pollutant detection model is calculated based on the thinking correction algorithm, and the model parameters of the initial atmospheric pollutant detection model are adjusted based on the optimal correction factor, so that the atmospheric pollutant detection model is obtained, the detection precision of the atmospheric pollutant detection model is improved, and the precision of the atmospheric pollutant detection method for the construction site is improved.
In some embodiments, before the inputting the image information into the preset machine type detection model, the method further comprises the steps of:
acquiring target image information from a preset image information database; the target image information is the target image information in the detected area acquired at a designated time before the acquisition time of the image information, and the target image information comprises a plurality of pieces;
inputting the atmospheric pollutant information, the image information and the target image information into a preset 3D convolutional neural network model; the 3D convolutional neural network model comprises a feature extraction layer, a feature comparison layer, a feature removal layer, an image information matching layer and an atmospheric pollutant information evaluation layer;
Respectively extracting image features of the image information and each target image information based on the feature extraction layer to obtain first image feature information and second image feature information; the first image feature information is image feature information corresponding to the image information, the second image feature information is image feature information corresponding to the target image information, and the second image feature information comprises a plurality of pieces of image feature information;
comparing the first image characteristic information and the second image characteristic information based on the characteristic comparison layer to obtain common image information between the first image characteristic information and the second image characteristic information;
removing the common image information in the first image characteristic information based on the characteristic removing layer to obtain target image characteristic information of the image information;
matching standard image information for the target image characteristic information in the image information based on the image information matching layer;
scoring the atmospheric contaminant information based on the standard image information through the atmospheric contaminant information evaluation layer to obtain a scoring value of the atmospheric contaminant information;
Comparing the scoring value with a preset scoring value;
and if the grading value is larger than the preset grading value, determining that the atmospheric pollutant information is accurate.
It will be appreciated that there is a static object within the detected region, the static object and its shadow generated by the static object being the common image information.
It will be appreciated that the standard image information comprises a plurality of standard image information blocks, each standard image information block representing image information of a standard atmospheric contaminant region from which profile information, position information and area information of the standard atmospheric contaminant region can be obtained.
When the atmospheric contaminant information is rated based on the standard image information, firstly, each standard image information block is respectively matched with the atmospheric contaminant information, then, whether each standard image information block is successfully matched with the atmospheric contaminant information is respectively judged, and finally, the ratio of the number of the standard image information blocks successfully matched to the total number of the standard image information blocks is used as the rated value.
The method is used for verifying the accuracy of the atmospheric pollutant information, and is beneficial to improving the accuracy of the method for detecting the atmospheric pollutant in the construction site.
In some embodiments, before sending the atmospheric contaminant detection report to the management platform, the method further comprises encrypting the atmospheric contaminant detection report, the method of encrypting the atmospheric contaminant detection report comprising the steps of:
dividing the image information according to a preset image information dividing rule to obtain a plurality of sub-image information, and sorting the sub-image information based on the position relation of the sub-image information in the image information to obtain a sub-image information sequence;
sequentially calculating gray values of each piece of sub-image information in the sub-image information sequence to obtain a gray value sequence, and extracting numbers of the gray values on bits according to each gray value of the gray value sequence to obtain a target array;
acquiring shooting time information of the image information and a coding sequence of the management platform;
acquiring an initial coding table matched with the shooting time information from a preset coding table database based on the shooting time information; wherein the initial encoding table comprises character columns and digital columns;
Taking the last digit of the shooting moment information as a target digit, and determining a target character in the coding sequence; wherein, the target character and the character of the character column belong to characters of different languages, and one target character exists in the coding sequence;
deleting the character corresponding to the target number in the initial coding table to obtain a first character space, and inserting the target character into the first character space to obtain an intermediate target coding table;
deleting a first character of a character column of the intermediate target coding table to obtain a second character space, moving each character after the second character space forward by one character space to obtain a third character space, and inserting the first character of the character column of the intermediate target coding table into the third character space to obtain a target coding table;
and carrying out coding processing on the target array based on the target coding table to obtain an encryption password, and carrying out encryption processing on the atmospheric pollutant detection report based on the encryption password to obtain an encrypted atmospheric pollutant detection report.
It should be noted that, in this embodiment, the initial encoding tables are set for different time periods in a day, and when the initial encoding table matched with the shooting time information is obtained from a preset encoding table database based on the shooting time information, the initial encoding table corresponding to the time period in which the shooting time is located is determined as the initial encoding table of the shooting time.
Illustratively, the gray value sequences are 135, 231, 197, 246, 172, 199, and the photographing time information is 12:35:46, the coding sequence of the management platform is 37r943d eta 456, the target array is 5, 1, 7, 6, 2 and 9, the target number is 6, the target character is eta, if the initial coding table matched with the shooting time information is shown in table 1, the intermediate target coding table is shown in table 2, the target coding table is shown in table 3, and the encryption password is eta UDMWG.
Table 1 initial encoding table
Table 2 intermediate target encoding table
Table 3 target encoding table
According to the method, the security of the atmospheric contaminant detection report is improved by conducting encryption processing on the atmospheric contaminant detection report, the atmospheric contaminant detection report can be prevented from being stolen by unauthorized persons, the initial encoding table matched with the shooting time information is obtained in the preset encoding table database based on the shooting time information, the initial encoding table is modified based on the target number and the target character, the target encoding table is obtained, instantaneity of the target encoding table is improved, different target encoding tables are obtained at different moments, the randomness of the encryption passwords is improved, and therefore the encryption effect of encrypting the atmospheric contaminant detection report is improved.
Referring to fig. 2, fig. 2 is a schematic block diagram illustrating a construction of a construction site atmospheric pollutant detection device 100 based on image analysis according to an embodiment of the present application, and as shown in fig. 2, the construction site atmospheric pollutant detection device 100 based on image analysis includes:
the first obtaining module 110 is configured to obtain, in real time, image information of a detected area through a preset image capturing device, and input the image information into a preset atmospheric contaminant detection model, so as to obtain atmospheric contaminant information in the detected area.
The first judging module 120 is configured to judge whether an atmospheric contaminant exists in the detected area based on the atmospheric contaminant information.
The input module 130 is configured to input the image information into a preset mechanical type detection model to obtain mechanical type information in the detected area if the detected area contains atmospheric pollutants;
and a second judging module 140, configured to judge whether a mechanical device exists in the detected area based on the mechanical type information.
And the processing module 150 is configured to perform association processing on the atmospheric contaminant information and the machine type information to obtain atmospheric contaminant-machine association information if a machine device exists in the detected area.
A second acquisition module 160 for acquiring contaminant concentration level information of the atmospheric contaminant based on the image information.
A first generation module 170 is configured to generate an atmospheric contaminant detection report for the detected area based on the atmospheric contaminant-mechanical association information and the contaminant concentration level information, and send the atmospheric contaminant detection report to a management platform.
And a second generating module 180, configured to generate alarm information based on the atmospheric contaminant detection report, and report the alarm information to staff in the detected area.
It should be noted that, for convenience and brevity of description, the specific working process of the apparatus and each module described above may refer to the corresponding process in the foregoing embodiment of the method for detecting atmospheric pollutants on a construction site based on image analysis, which is not described herein.
The apparatus 100 for detecting atmospheric pollutants at a worksite based on image analysis provided in the above-described embodiment may be implemented in the form of a computer program that can be run on the terminal device 200 as shown in fig. 3.
Referring to fig. 3, fig. 3 is a schematic block diagram of a structure of a terminal device 200 according to an embodiment of the present application, where the terminal device 200 includes a processor 201 and a memory 202, and the processor 201 and the memory 202 are connected through a system bus 203, and the memory 202 may include a nonvolatile storage medium and an internal memory.
The non-volatile storage medium may store a computer program. The computer program comprises program instructions that, when executed by the processor 201, cause the processor 201 to perform any of the above-described worksite atmospheric contaminant detection based on image analysis.
The processor 201 is used to provide computing and control capabilities supporting the operation of the overall terminal device 200.
The internal memory provides an environment for the execution of a computer program in a non-volatile storage medium that, when executed by the processor 201, causes the processor 201 to perform any of the above-described method of worksite atmospheric contaminant detection based on image analysis.
It will be appreciated by those skilled in the art that the structure shown in fig. 3 is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation of the terminal device 200 related to the present application, and that a specific terminal device 200 may include more or less components than those shown in the drawings, or may combine some components, or have a different arrangement of components.
It should be appreciated that the processor 201 may be a central processing unit (Central Processing Unit, CPU), and the processor 201 may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. Wherein the general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
In some embodiments, the processor 201 is configured to execute a computer program stored in the memory to implement the following steps:
acquiring image information of a detected area in real time through a preset camera device, and inputting the image information into a preset atmospheric pollutant detection model to obtain atmospheric pollutant information in the detected area;
judging whether the atmospheric pollutants exist in the detected area or not based on the atmospheric pollutant information;
if the detected area contains atmospheric pollutants, inputting the image information into a preset mechanical type detection model to obtain mechanical type information in the detected area;
judging whether mechanical equipment exists in the detected area or not based on the mechanical type information;
if mechanical equipment exists in the detected area, carrying out association processing on the atmospheric pollutant information and the mechanical type information to obtain atmospheric pollutant-mechanical association information;
acquiring pollutant concentration level information of the atmospheric pollutants based on the image information;
generating an atmospheric contaminant detection report of the detected area based on the atmospheric contaminant-mechanical association information and the contaminant concentration level information, and transmitting the atmospheric contaminant detection report to a management platform;
And generating alarm information based on the atmospheric pollutant detection report, and broadcasting the alarm information to staff in the detected area.
Embodiments of the present application also provide a computer-readable storage medium storing a computer program that, when executed by one or more processors, causes the one or more processors to implement a method for detecting a site atmospheric contaminant based on image analysis as provided in the embodiments of the present application.
The computer readable storage medium may be an internal storage unit of the terminal device 200 of the foregoing embodiment, for example, a hard disk or a memory of the terminal device 200. The computer readable storage medium may also be an external storage device of the terminal device 200, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which the terminal device 200 is equipped with.
While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (9)
1. A method for detecting atmospheric pollutants at a worksite based on image analysis, the method comprising:
acquiring image information of a detected area in real time through a preset camera device, and inputting the image information into a preset atmospheric pollutant detection model to obtain atmospheric pollutant information in the detected area;
judging whether the atmospheric pollutants exist in the detected area or not based on the atmospheric pollutant information;
if the detected area contains atmospheric pollutants, inputting the image information into a preset mechanical type detection model to obtain mechanical type information in the detected area;
judging whether mechanical equipment exists in the detected area or not based on the mechanical type information;
if mechanical equipment exists in the detected area, carrying out association processing on the atmospheric pollutant information and the mechanical type information to obtain atmospheric pollutant-mechanical association information;
acquiring pollutant concentration level information of the atmospheric pollutants based on the image information;
generating an atmospheric contaminant detection report of the detected area based on the atmospheric contaminant-mechanical association information and the contaminant concentration level information, and transmitting the atmospheric contaminant detection report to a management platform;
And generating alarm information based on the atmospheric pollutant detection report, and broadcasting the alarm information to staff in the detected area.
2. The method for detecting atmospheric pollutants at a worksite based on image analysis according to claim 1, wherein the training method for the atmospheric pollutant detection model comprises the following steps:
acquiring a training sample set; the training sample set comprises a plurality of mapping relations, wherein the mapping relations are mapping relations between training image information and actual atmospheric pollutant information corresponding to the training image information, the training image information is image information acquired for the detected area, and the actual atmospheric pollutant information comprises actual atmospheric pollutant types in the training image information and actual area coverage information of atmospheric pollutants corresponding to the actual atmospheric pollutant types in the detected area;
dividing the training sample set into a training set and a correction set;
constructing a YoloV5 network model by adopting a YoloV5 algorithm, and training the YoloV5 network model based on the training set to obtain an initial atmospheric pollutant detection model;
respectively inputting the training image information in the correction set into the initial atmospheric pollutant detection model to obtain predicted atmospheric pollutant information of the training image information;
Constructing a mapping relation table of predicted atmospheric pollutant information and actual atmospheric pollutant information based on the predicted atmospheric pollutant information and the actual atmospheric pollutant information corresponding to the training image information in the correction set;
acquiring a thinking correction algorithm of the initial atmospheric pollutant detection model based on the mapping relation table of the predicted atmospheric pollutant information and the actual atmospheric pollutant information;
calculating an optimal correction factor of the initial atmospheric contaminant detection model based on the thought correction algorithm;
and adjusting model parameters of the initial atmospheric pollutant detection model based on the optimal correction factors to obtain the atmospheric pollutant detection model.
3. The method for detecting atmospheric pollutants at a worksite based on image analysis according to claim 1, wherein the atmospheric pollutant information includes an atmospheric pollutant type in the detected area and first area coverage information of atmospheric pollutants corresponding to the atmospheric pollutant types respectively in the detected area, the machine type information includes a machine type in the detected area and second area coverage information of mechanical equipment corresponding to the machine types respectively in the detected area, wherein each of the atmospheric pollutant types corresponds to at least one of the first area coverage information, each of the machine types corresponds to at least one of the second area coverage information, and the correlating the atmospheric pollutant information and the machine type information includes:
Judging whether target second area coverage information corresponding to the first area coverage information exists in the second area coverage information according to the first area coverage information; wherein the mechanical equipment corresponding to the target second area coverage information and the atmospheric pollutants corresponding to the first area coverage information have overlapping coverage areas in the detected area;
for each piece of first area coverage information, if target second area coverage information corresponding to the first area coverage information exists in each piece of second area coverage information, determining first target associated mechanical equipment of the atmospheric pollutants corresponding to the first area coverage information based on the first area coverage information and each piece of target second area coverage information, and associating the atmospheric pollutants corresponding to the first area coverage information with the first target associated mechanical equipment; wherein the first target associated mechanical device is the mechanical device having a largest overlapping coverage area of the atmospheric contaminants within the detected area corresponding to the first area coverage information;
For each piece of first area coverage information, if target second area coverage information corresponding to the first area coverage information does not exist in each piece of second area coverage information, determining second target associated mechanical equipment based on the first area coverage information and each piece of target second area coverage information, calculating distance values between first designated position information of the second target associated mechanical equipment and each piece of second designated position information of a coverage area of the atmospheric pollutants in the detected area corresponding to the first area coverage information respectively, comparing each distance value with a preset distance value respectively, and associating the atmospheric pollutants corresponding to the first area coverage information with the second target associated mechanical equipment if any distance value is smaller than the preset distance value; the second target related mechanical equipment is the mechanical equipment above the center of the coverage area of the atmospheric pollutants in the detected area corresponding to the coverage information of the first area.
4. The method for detecting atmospheric pollutants at a worksite based on image analysis according to claim 3, wherein the acquiring the pollutant concentration level information of the atmospheric pollutants based on the image information comprises:
A Gaussian mixture model is adopted to construct a background removal model, and the image information is input into the background removal model to remove the image information irrelevant to the atmospheric pollutants in the image information, so as to obtain the image information of the atmospheric pollutants;
and for each first area coverage information, if the atmospheric pollutants corresponding to the first area coverage information exist in the first target related mechanical equipment or the second target related mechanical equipment, acquiring an average gray value of an image of a coverage area of the atmospheric pollutants corresponding to the first area coverage information in the detected area based on the atmospheric pollutant image information, and determining a pollutant concentration level of the atmospheric pollutants corresponding to the first area coverage information based on the average gray value.
5. The method for detecting atmospheric pollutants at a worksite based on image analysis according to claim 1, further comprising, prior to said inputting said image information into a predetermined machine type detection model:
acquiring target image information from a preset image information database; the target image information is the target image information in the detected area acquired at a designated time before the acquisition time of the image information, and the target image information comprises a plurality of pieces;
Inputting the atmospheric pollutant information, the image information and the target image information into a preset 3D convolutional neural network model; the 3D convolutional neural network model comprises a feature extraction layer, a feature comparison layer, a feature removal layer, an image information matching layer and an atmospheric pollutant information evaluation layer;
respectively extracting image features of the image information and each target image information based on the feature extraction layer to obtain first image feature information and second image feature information; the first image feature information is image feature information corresponding to the image information, the second image feature information is image feature information corresponding to the target image information, and the second image feature information comprises a plurality of pieces of image feature information;
comparing the first image characteristic information and the second image characteristic information based on the characteristic comparison layer to obtain common image information between the first image characteristic information and the second image characteristic information;
removing the common image information in the first image characteristic information based on the characteristic removing layer to obtain target image characteristic information of the image information;
Matching standard image information for the target image characteristic information in the image information based on the image information matching layer;
scoring the atmospheric contaminant information based on the standard image information through the atmospheric contaminant information evaluation layer to obtain a scoring value of the atmospheric contaminant information;
comparing the scoring value with a preset scoring value;
and if the grading value is larger than the preset grading value, determining that the atmospheric pollutant information is accurate.
6. The method of image analysis based worksite atmospheric contaminant detection according to claim 1, wherein prior to sending the atmospheric contaminant detection report to the management platform, the method further comprises encrypting the atmospheric contaminant detection report, the method of encrypting the atmospheric contaminant detection report comprising:
dividing the image information according to a preset image information dividing rule to obtain a plurality of sub-image information, and sorting the sub-image information based on the position relation of the sub-image information in the image information to obtain a sub-image information sequence;
Sequentially calculating gray values of each piece of sub-image information in the sub-image information sequence to obtain a gray value sequence, and extracting numbers of the gray values on bits according to each gray value of the gray value sequence to obtain a target array;
acquiring shooting time information of the image information and a coding sequence of the management platform;
acquiring an initial coding table matched with the shooting time information from a preset coding table database based on the shooting time information; wherein the initial encoding table comprises character columns and digital columns;
taking the last digit of the shooting moment information as a target digit, and determining a target character in the coding sequence; wherein, the target character and the character of the character column belong to characters of different languages, and one target character exists in the coding sequence;
deleting the character corresponding to the target number in the initial coding table to obtain a first character space, and inserting the target character into the first character space to obtain an intermediate target coding table;
deleting a first character of a character column of the intermediate target coding table to obtain a second character space, moving each character after the second character space forward by one character space to obtain a third character space, and inserting the first character of the character column of the intermediate target coding table into the third character space to obtain a target coding table;
And carrying out coding processing on the target array based on the target coding table to obtain an encryption password, and carrying out encryption processing on the atmospheric pollutant detection report based on the encryption password to obtain an encrypted atmospheric pollutant detection report.
7. A worksite atmospheric pollutant detection device based on image analysis, characterized by comprising:
the first acquisition module is used for acquiring image information of a detected area in real time through a preset camera device, inputting the image information into a preset atmospheric pollutant detection model and obtaining atmospheric pollutant information in the detected area;
the first judging module is used for judging whether the atmospheric pollutants exist in the detected area or not based on the atmospheric pollutant information;
the input module is used for inputting the image information into a preset mechanical type detection model to obtain mechanical type information in the detected area if the detected area contains atmospheric pollutants;
the second judging module is used for judging whether mechanical equipment exists in the detected area or not based on the mechanical type information;
the processing module is used for carrying out association processing on the atmospheric pollutant information and the mechanical type information if mechanical equipment exists in the detected area, so as to obtain atmospheric pollutant-mechanical association information;
A second acquisition module for acquiring contaminant concentration level information of the atmospheric contaminant based on the image information;
the first generation module is used for generating an atmospheric pollutant detection report of the detected area based on the atmospheric pollutant-mechanical association information and the pollutant concentration level information and sending the atmospheric pollutant detection report to a management platform;
and the second generation module is used for generating alarm information based on the atmospheric pollutant detection report and broadcasting the alarm information to staff in the detected area.
8. A terminal device comprising a processor, a memory and a computer program stored on the memory and executable by the processor, wherein the computer program when executed by the processor implements the method for detecting atmospheric pollutants at a worksite based on image analysis according to any one of claims 1 to 6.
9. A computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, wherein the computer program, when executed by a processor, implements the method for detecting atmospheric pollutants at a worksite based on image analysis according to any one of claims 1 to 6.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150362370A1 (en) * | 2014-06-12 | 2015-12-17 | Khaled Ali Abuhasel | Sensor-free image-based and computationally effective approach to vehicular, industrial and domestic air pollution estimation and control |
| CN109035298A (en) * | 2018-09-05 | 2018-12-18 | 中南大学 | A kind of black smoke detection and DG calculation method based on black smoke kinetic characteristic |
| CN110567510A (en) * | 2019-07-23 | 2019-12-13 | 北京英视睿达科技有限公司 | Atmospheric pollution monitoring method, system, computer equipment and storage medium |
| CN112733732A (en) * | 2021-01-12 | 2021-04-30 | 深圳市飞瑞斯科技有限公司 | Face detection and recognition method based on feature analysis |
| CN113657305A (en) * | 2021-08-20 | 2021-11-16 | 深圳技术大学 | Video-based intelligent detection method for blackness level of black smoke vehicle and Ringelmann |
| CN115147642A (en) * | 2022-06-02 | 2022-10-04 | 盛视科技股份有限公司 | Method and device for detecting muck vehicle based on vision, computer and storage medium |
-
2024
- 2024-01-02 CN CN202410001588.6A patent/CN117496449B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150362370A1 (en) * | 2014-06-12 | 2015-12-17 | Khaled Ali Abuhasel | Sensor-free image-based and computationally effective approach to vehicular, industrial and domestic air pollution estimation and control |
| CN109035298A (en) * | 2018-09-05 | 2018-12-18 | 中南大学 | A kind of black smoke detection and DG calculation method based on black smoke kinetic characteristic |
| CN110567510A (en) * | 2019-07-23 | 2019-12-13 | 北京英视睿达科技有限公司 | Atmospheric pollution monitoring method, system, computer equipment and storage medium |
| CN112733732A (en) * | 2021-01-12 | 2021-04-30 | 深圳市飞瑞斯科技有限公司 | Face detection and recognition method based on feature analysis |
| CN113657305A (en) * | 2021-08-20 | 2021-11-16 | 深圳技术大学 | Video-based intelligent detection method for blackness level of black smoke vehicle and Ringelmann |
| CN115147642A (en) * | 2022-06-02 | 2022-10-04 | 盛视科技股份有限公司 | Method and device for detecting muck vehicle based on vision, computer and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 汪新;MCNAMARA K F;: "风洞模拟建筑物对大气污染物扩散影响的若干问题讨论", 实验流体力学, no. 03, 30 September 2006 (2006-09-30), pages 65 - 72 * |
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