LU501796B1 - Intelligent calculation method of multi-camera earthwork coverage based on blockchain technology - Google Patents
Intelligent calculation method of multi-camera earthwork coverage based on blockchain technology Download PDFInfo
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Abstract
The application relates to an intelligent calculation method of multi-camera earthwork coverage based on blockchain technology, which belongs to the field of intelligent buildings. the intelligent calculation method of multi-camera earthwork coverage based on blockchain technology includes obtaining image acquisition information, processing the image acquisition information and outputting image processing information; matching the image processing information according to an image matching algorithm to obtain overlapping area information, wherein the overlapping area information refers to areas where images shot by a plurality of cameras overlap each other; obtaining coverage area information and earthwork area information according to the image processing information and overlapping area information; the earthwork coverage rate is obtained by calculating the coverage area information and earthwork area information. The application has the effect of improving the accuracy of the calculation result of earthwork coverage in the construction site.
Description
DESCRIPTION LU501796 INTELLIGENT CALCULATION METHOD OF MULTI-CAMERA EARTHWORK
TECHNICAL FIELD The application relates to the field of intelligent buildings, in particular to an intelligent calculation method of multi-camera earthwork coverage based on blockchain technology.
BACKGROUND With the development of science and technology, when people carry out engineering construction, the area of the construction site becomes larger and larger. For the sake of environmental protection, it is necessary to cover the construction site with earthwork in order to avoid dust pollution caused by earthwork excavation.
At present, managers will pre-judge the area to be covered according to the construction model of the construction site, and purchase covering equipment of corresponding size for earthwork covering according to the judged result.
In view of the related technologies mentioned above, the inventor found that the calculation result of earthwork coverage at the construction site was very inaccurate and the error was large when the earthwork coverage at the construction site was calculated by the above method.
SUMMARY The application provides an intelligent calculation method of multi-camera earthwork coverage based on blockchain technology, which has the characteristics of improving the accuracy of earthwork coverage calculation results at the construction site.
The first purpose of this application is to provide an intelligent calculation method of multi-camera earthwork coverage based on blockchain technology.
The first application purpose of this application is achieved by the following technical scheme: An intelligent calculation method of multi-camera earthwork coverage based on blockchain technology includes: Acquiring image acquisition information, processing the image acquisition information and outputting image processing information;
Matching the image processing information according to an image matching algorithm t&J501796 obtain overlapping area information, wherein the overlapping area information refers to areas where images shot by a plurality of cameras overlap each other; Obtaining coverage area information and earthwork area information according to the image processing information and overlapping area information; The earthwork coverage rate is obtained by calculating the coverage area information and earthwork area information.
By adopting the technical scheme, firstly, the image information captured by cameras is acquired and processed, then the overlapping area information of images is calculated according to the images captured by multiple cameras, then the coverage area information and earthwork area information are obtained according to the overlapping area information and image processing information, and then the earthwork coverage can be obtained after the two are calculated. In this way, the problem that the images captured by multiple cameras are overlapped and inconvenient to calculate can be solved according to the overlapping area information, and the accuracy of the earthwork coverage calculation result at the construction site is improved.
In a preferred example, the application can be further configured as follows: the method for acquiring image collection information includes acquiring image collection information through a blockchain platform.
By adopting the above technical scheme, it is more convenient to obtain information through the blockchain platform, and because of the characteristics of the blockchain itself, when a single node has a problem, the whole data will not be affected, and the data security is improved.
In a preferred example, the application can be further configured as follows: The method for outputting image processing information after processing image acquisition information comprises the following steps: Extracting key frame images; Image processing information is obtained after image processing the key frame image; Among them, image processing includes image size processing, normalization processing and zero center processing.
By adopting the above technical scheme, the image quality can be improved, and thé&J501796 influence of some extra factors on key frame images can be avoided.
In a preferred example, the application can be further configured that the output image processing information needs to be judged, and the judging process includes: judging the key frame images in the image processing information by using a neural network method based on deep learning, and if the judgment result is yes, matching the image processing information according to the image matching algorithm.
By adopting the technical scheme, the key frame images are screened, the resource waste of useless images is reduced, and the calculation efficiency is improved.
In a preferred example, the application can be further configured as follows: The method for matching image processing information according to the image matching algorithm comprises the following steps: Acquiring the parameter information of the camera; Preprocessing the image processing information according to the parameter information of the camera.
In a preferred example, the application can be further configured as follows: the method for matching image processing information according to the image matching algorithm includes further matching the preprocessed image processing information by using the image matching algorithm based on SIFT features.
In a preferred example, the application can be further configured as follows: the method for detecting and analyzing image processing information according to the image detection algorithm includes: Transforming the key frame images in the image processing information into HSV color space; After color processing and detection of key frame images in HSV color space, coverage area information and earthwork area information are obtained.
The second purpose of this application is to provide a multi-camera earthwork coverage calculation system based on blockchain technology.
The second application purpose of this application is achieved by the following technical scheme:
A multi-camera earthwork coverage calculation system based on blockchain technology)501796 includes: The processing module 1s used for acquiring image acquisition information, processing the image acquisition information and outputting image processing information; The matching module is used for matching the image processing information according to the image matching algorithm to obtain overlapping area information; The detection module is used for obtaining coverage area information and earthwork area information according to the image processing information and overlapping area information; The calculation module is used for calculating the coverage area information and the earthwork area information to obtain the earthwork coverage rate.
The third purpose of this application 1s to provide an intelligent terminal.
The third application purpose of this application is achieved by the following technical scheme: An intelligent terminal comprises a memory and a processor, wherein the memory 1s stored with computer program instructions of the intelligent calculation method of multi-camera earthwork coverage based on blockchain technology which can be loaded and executed by the processor.
The fourth purpose of this application is to provide a computer medium that can store corresponding programs.
The fourth application purpose of this application is achieved by the following technical scheme: A computer-readable storage medium stores a computer program that can be loaded by a processor and execute any of the above-mentioned intelligent calculation methods of multi-camera earthwork coverage based on blockchain technology.
To sum up, this application includes at least one of the following beneficial technical effects: The image information captured by cameras is processed, and the overlapping area information of images captured by multiple cameras is calculated according to the processed image information. Then, the coverage area information and earthwork area information are obtained by analyzing the overlapping area information and image processing information according to the algorithm. In this way, the accuracy of earthwork coverage calculation results 14501796 construction site can be improved.
BRIEF DESCRIPTION OF THE FIGURES Fig. 1 is a schematic diagram of the system structure of an embodiment of this application.
Fig. 2 is a schematic diagram of the method flow of the embodiment of the application.
Fig. 3 is a flow diagram of an image matching algorithm based on SIFT features in the embodiment of this application.
Description of the drawings: 1. Processing module; 2. Matching module; 3. Detection module; 4. Calculation module.
DESCRIPTION OF THE INVENTION The application will be described in further detail below with reference to the attached drawings.
This specific example is only an explanation of this application, and it is not a limitation of this application. After reading this specification, the person in the field can make changes to this example without creative contribution as needed, but it is protected by the patent law as long as it falls within the scope of the claims of this application.
In order to make the purpose, technical scheme and advantages of this application embodiment clearer, the technical scheme in this application embodiment will be clearly and completely described below with reference to the drawings in this application embodiment. Obviously, the described embodiments are part of this application embodiment, not all of it. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
The embodiment of this application will be described in further detail below with reference to the drawings in the specification.
The application provides a multi-camera earthwork coverage calculation system based on blockchain technology. As shown in Figure 1, a multi-camera earthwork coverage calculation system based on blockchain technology includes a processing module 1, a matching module 2, a detection module 3 and a calculation module 4. The processing module 1 is used for acquiring image acquisition information, processing the image acquisition information and outputting image processing information, Matching module 2, which can be used to match the imagé/501796 processing information according to the image matching algorithm to obtain overlapping area information; The detection module 3 is used for obtaining coverage area information and earthwork area information according to the image processing information and overlapping area information; The calculation module 4 is used for calculating the coverage area information and the earthwork area information to obtain the earthwork coverage rate.
In the embodiment of this application, the above modules are all part of the functional modules in the server. The server acquires image acquisition information through communication with the blockchain platform, and then preprocesses the image acquisition information, such as the size and definition of the image. The server preprocesses the image acquisition information and outputs image processing information, At this time, it is necessary to judge the image processing information. Because the cameras set in the construction site monitor the situation of the construction site in real time, the images in some special periods, such as the night period, do not need to be processed.
According to the image matching algorithm, the image processing information is matched to obtain the overlapping area information, then the coverage area information and earthwork area information are obtained according to the image processing information and the overlapping area information, then the earthwork coverage rate of the construction site is calculated according to the coverage area information and the earthwork area information, then the areas to be covered and the whole earthwork area are calculated according to the overlapping areas, and then the earthwork coverage rate can be obtained by calculating the two. In this way, the accuracy of the earthwork coverage rate calculation results of the construction site can be improved.
The application also provides an intelligent calculation method of multi-camera earthwork coverage based on blockchain technology, and the main flow of the method is described as follows.
As shown in Figure 2: S101: acquire image acquisition information, process the image acquisition information and output image processing information.
In the embodiment of this application, the image acquisition information refers to the imag&/501 796 information acquired by the camera; At the construction site, multiple cameras will be set to monitor the construction site in real time; Among them, the way to acquire image collection information can be to communicate with the blockchain platform, so as to acquire image collection information; Blockchain database is distributed storage, and each node can copy and store a database copy. If a node fails, it will not affect the whole database technology. Moreover, the information confirmed in the blockchain can no longer be modified or deleted, which is more secure than the centralized database.
In the embodiment of this application, the server will acquire some key information while acquiring the image acquisition information, where the key information can be understood as some parameter information about the camera itself, such as the position, orientation, rotatable angle, etc. Through these key information, the coverage area generated by overlapping the areas monitored by multiple cameras can be preliminarily judged, which is convenient for the next operation. Understandably, the key information is based on the information generated by the camera itself. When the camera is installed, the key information about the camera has been uploaded to the blockchain platform. When in use, the image acquisition information and key information about the camera can be obtained only by communication between the server and the blockchain platform.
After acquiring the image acquisition information, it is necessary to preprocess the image acquisition information and output the image processing information after processing, The pretreatment includes the following steps: S100: Extracting key frame images.
S200: image processing information is obtained after image processing the key frame image.
Key-frame images can be understood as key-frame images except those in some special periods or situations. The special period here refers to the nighttime period and bad weather, such as dusty weather or rainy weather. In the special period, the line of sight of the camera is blocked, and the captured image information is also a vague image, which cannot be judged as a key frame image. Special circumstances can be understood as the frame image is damaged, and the valid image cannot be displayed.
In the embodiment of this application, the requirement for the number of extracted kéyJ501796 frame images is low, for example, one key frame image is extracted every ten minutes; After extracting the key frame image, it is necessary to further process the key frame image. The image processing methods include: image size processing, normalization processing and zero centering processing; In order to obtain images suitable for matching, the above image processing methods are adopted. Among them, image size processing can be understood as processing the size of key frame images to make the size of key frame images meet the processing requirements; Normalization processing means that the image data in the key frame image is limited to the required range after processing, so as to facilitate the processing of image data and ensure the convergence speed when the program runs; Zero-center processing means that the centers of all the processed image data in the key frame images are (0,0), and the three image processing methods adopted in this application embodiment are common technical means in this field, which will not be repeated here.
Image processing information is obtained after processing the image acquisition information, and the image at this time is still a key frame image, but the key frame image is a key frame image after image processing; In the above process, there is a problem that the image in special period or special circumstances may not be able to perform the next step operation. Although the key frame images have been preprocessed, there may still be some cases where the key frame images cannot be matched, so it is necessary to judge the key frame images in the image processing information, and when the judgment result is yes, the key frame images can be processed in the next step.
In the embodiment of this application, the neural network model based on deep learning is used to judge the key frame images in the image processing information; Firstly, the neural network model is established, and then the neural network model is trained in a data-driven way, so that the trained neural network model can be obtained. Among them, the input of the neural network model is the key frame image, the output is the judgment result, and the judgment result is only yes or no, When the judgment result is yes, proceed to the next operation; when the judgment result is no, it means that the frame image does not need to be processed; Understandably, deep learning refers to image deep learning algorithms in related technologies,
such as VGG, ResNET algorithm, etc. The above process is a common technical means in thi$J501796 field, and will not be repeated here.
S102, matching the image processing information according to the image matching algorithm to obtain overlapping area information.
When the judgment result is yes, the image processing information is processed in step S102.
Image matching algorithm is an intelligent image processing technology. Image matching algorithm includes many algorithms, such as image matching method based on gray scale and template. The principle of this matching method is to find sub-images similar to template images from another image according to known template images. Another example is feature-based image matching methods, such as point features and edge features. Another example is image matching algorithm based on domain transform, such as Fourier-Merlin transform, Walsh transform and wavelet transform.
Pre-processing is required before matching the image processing information, which has already been carried out in step S101, that is, acquiring the parameter information of the camera, and then pre-processing the image processing information according to the parameter information of the camera; The parameter information here refers to the position, orientation and rotatable angle of the camera. According to these parameter information, the image is preprocessed, and then the image is matched.
In this application embodiment, the image matching algorithm based on SIFT features is adopted, and the specific steps of this matching algorithm are introduced below.
As shown in Figure 3: S1: acquiring image processing information.
S2, establishing a scale space.
By convolving the input image with different Gaussian kernels, the Gaussian scale space of the image is obtained, and the images of two adjacent scale spaces are subtracted to obtain the Gaussian difference scale space.
S3, extracting feature points.
In Gaussian difference scale space, each detection point is compared with 26 adjacent points to determine whether it is the maximum or minimum point, and ensure that the detection point is the extreme point in scale space and image space, in which all local extreme points constitute théJ501796 key point set of SIFT feature.
S4: extracting feature descriptors.
Using the gradient direction distribution characteristics of pixels in the neighborhood of key points to specify direction parameters for each key point, the operator has rotation invariance.
Among them, the modulus and direction formula of the gradient at (x, y) are calculated by the formula.
J Todas 0 —Lt{—-1 FIR +13 SLR, F-LF N N HL tx ÿ+13 —L {x ÿ— 13 ]5 SO (x WO tan” | . : IL {x+1i, vd —Lœ—f ÿ D M(x,y) is the key point gradient modulus, © (x,y) is the key point gradient direction, and L is the key point scale; Taking the key points as the center, sampling and dividing the window statistics can get the 128-dimensional SIFT feature vector of feature points.
SS, pre-matching feature point pairs.
The optimal node first search algorithm is used to match the feature point vectors of different camera key frame images, and the set threshold is used to determine the matching pair.
S6: removal of external points.
Use RANSAC algorithm to eliminate mismatched point pairs, estimate by randomly sampling one point for many times, and determine the model parameters by sampling multiple data each time, to judge whether the matching point pairs are within the allowable error range, and delete mismatched point pairs in the process.
Through the above process, image matching is carried out, and the location and size of the matching feature point pair area are calculated and obtained, so that the multi-camera overlapping area can be obtained, where the multi-camera overlapping area refers to the overlapping area information.
The image matching algorithm based on SIFT features is used to match the key frame images in the image processing information, so as to calculate the overlapping area information. This method can accurately calculate the overlapping area information shot by multiple cameras, so as to carry out the next operation according to the overlapping area information. By using this method, the calculation accuracy of the overlapping area information is improved, and then thé&J501796 accuracy of the calculation results of earthwork coverage in the construction site is improved.
S103: obtain coverage area information and earthwork area information according to the image processing information and overlapping area information.
In this step, the specific method of obtaining coverage area information and earthwork area information according to the image processing information and overlapping area information is to convert the key frame images in the image processing information into HSV color space, and then use the color attributes of the coverage net and soil to detect the coverage area and the earthwork part, so as to obtain coverage area information and earthwork area information. The detection process is described in detail below.
1. Get the key frame images in the image processing information.
2. Convert the key frame image to HSV color space.
Color space is also called color model, color space or color system. Its purpose is to explain color in a generally acceptable way under some standards. There are many kinds of color space, such as RGB model, CMY model, HSV model, HSI model and so on. It should be noted that the traditional image is RGB model, which is a space defined according to other colors recognized by human eyes and can represent most colors. HSV model is put forward for better digital color processing; In image processing, the most commonly used color space is RGB model, which is often used for color display and image processing, and the three-dimensional coordinate model is easy to understand. HSV model is a color model for users' perception, which focuses on color representation, what color, depth, brightness and so on.
The way of conversion here is to complete the conversion by calculating each pixel in the key-frame image. The specific process of conversion is to read the RGB model into the workspace and display the image. By forcibly converting the data types of the pixel data in the RGB model, the data types in the RGB model are converted into the data types in the HSV model, thus realizing the conversion of the key-frame image into the HSV color space.
3. Filter the key frame images in HSV color space.
In the embodiment of this application, the covering equipment selected in the construction site is green covering net, and the color of earthworks is generally between yellow and brown, so that the key frame images can be filtered through the known green covering net and the approximate color range of earthworks, and the images can be filtered through the color rangleV501 796 so that the positions of the corresponding areas can be obtained.
4. Get the contour of all areas according to the edge extraction algorithm.
Contour extraction refers to extracting the edge of an object. For example, for a rectangular figure, the rectangular frame is the outer contour of the figure.
5. Calculate the area of each area.
6. Filter out the noise area with small area according to the preset threshold.
According to the area of each area calculated in step 5, compare the area of each area with the threshold value, and remove the area if the area is less than the threshold value.
The detection method adopted in this application example is a simple color space discrimination method, and its principle is to distinguish the coverage area from the bare earthwork area according to different colors.
In the embodiment of this application, the above-mentioned ways of obtaining coverage area and earthwork area can be realized by software, and the conversion of key frame images from RGB model to HSV model can also be realized by software, which are common technical means in this field, and will not be repeated here.
S104, calculating the coverage area information and the earthwork area information to obtain the earthwork coverage rate.
In the embodiment of this application, the coverage area information refers to the area information covered by the green coverage network, and the earthwork coverage rate can be obtained by calculating the coverage area information and the earthwork area information.
Using this method to calculate the earthwork coverage rate can improve the accuracy of the calculation result of earthwork coverage rate in the construction site.
In order to better execute the program of the above method, the application also provides an intelligent terminal, which comprises a memory and a processor.
The memory can be used to store instructions, programs, codes, code sets or instruction sets. The memory can include a storage program area and a storage data area, wherein the storage program area can store instructions for realizing an operating system, instructions for at least one function, instructions for realizing the above-mentioned intelligent calculation method of multi-camera earthwork coverage based on blockchain technology, etc. The data area can store the data involved in the intelligent calculation method of multi-camera earthwork coverage baséd/501 796 on blockchain technology.
A processor may include one or more processing cores. By running or executing the instructions, programs, code sets or instruction sets stored in the memory, the processor calls the data stored in the memory to perform various functions and process the data. The processor can be at least one of an application specific integrated circuit, a digital signal processor, a digital signal processing device, a programmable logic device, a field programmable gate array, a central processing unit, a controller, a microcontroller and a microprocessor. Understandably, for different devices, the electronic devices used to realize the above processor functions can be other devices, and the embodiments of this application are not specifically limited.
The application also provides a computer-readable storage medium, including, for example, U disk, removable hard disk, Read Only Memory (ROM), Random Access Memory (RAM), magnetic disk or optical disk and other media that can store program codes. The computer-readable storage medium stores a computer program that can be loaded by the processor and execute the above-mentioned intelligent calculation method of multi-camera earthwork coverage based on blockchain technology.
The above description is only the preferred embodiment of this application and the explanation of the applied technical principles. Those skilled in the art should understand that the disclosure scope involved in this application is not limited to the technical scheme formed by the specific combination of the above-mentioned technical features, but also covers other technical schemes formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned disclosure concept. For example, the above features are mutually replaced with the technical features with similar functions disclosed in this application (but not limited to).
Claims (7)
1. An intelligent calculation method of multi-camera earthwork coverage based on blockchain technology, characterized by comprising: acquiring image acquisition information, processing the image acquisition information and outputting image processing information; matching the image processing information according to an image matching algorithm to obtain overlapping area information, wherein the overlapping area information refers to areas where images shot by a plurality of cameras overlap each other; obtaining coverage area information and earthwork area information according to the image processing information and overlapping area information; the earthwork coverage rate is obtained by calculating the coverage area information and earthwork area information.
2. The intelligent calculation method of multi-camera earthwork coverage based on blockchain technology according to claim 1, characterized in that the method for acquiring image acquisition information comprises: acquiring image acquisition information through blockchain platform.
3. The intelligent calculation method of multi-camera earthwork coverage based on blockchain technology according to claim 1, characterized in that the method for processing image acquisition information and outputting image processing information comprises: extracting key frame images; image processing information is obtained after image processing the key frame image; among them, image processing includes image size processing, normalization processing and zero center processing.
4. The intelligent calculation method of multi-camera earthwork coverage based on blockchain technology according to claim 3, characterized in that the output image processing information needs to be judged, and the judging process includes: judging the key frame images in the image processing information by using a neural network method based on deep learning, and if the judgment result is yes, matching the image processing information according to the image matching algorithm.
5. The intelligent calculation method of multi-camera earthwork coverage based d:/501796 blockchain technology according to claim 1, wherein the method of matching image processing information according to image matching algorithm comprises: acquiring the parameter information of the camera; preprocessing the image processing information according to the parameter information of the camera.
6. The intelligent calculation method of multi-camera earthwork coverage based on blockchain technology according to claim 5, characterized in that the method of matching image processing information according to image matching algorithm comprises: further matching the preprocessed image processing information by using the image matching algorithm based on SIFT features.
7. The intelligent calculation method of multi-camera earthwork coverage based on blockchain technology according to claim 1, wherein the method of detecting and analyzing image processing information according to image detection algorithm comprises: transforming the key frame images in the image processing information into hsv color space; after color processing and detection of key frame images in HSV color space, coverage area information and earthwork area information are obtained.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115994442A (en) * | 2022-11-18 | 2023-04-21 | 湖南科大天河通信股份有限公司 | Alarm ringing sound coverage area and coverage rate algorithm |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115994442A (en) * | 2022-11-18 | 2023-04-21 | 湖南科大天河通信股份有限公司 | Alarm ringing sound coverage area and coverage rate algorithm |
| CN115994442B (en) * | 2022-11-18 | 2024-03-19 | 湖南科大天河通信股份有限公司 | Alarm ringing sound coverage area and coverage rate calculation method |
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