A method and system for detecting and identifying a riding stamp for power meter inspection

Abstract

The embodiment of the application provides a riding seal detection and identification method and system for power meter inspection, which contains image processing methods such as position positioning of a target area of a riding seal and a sealing bean of a power meter device, Hough circle transformation and polar coordinate transformation. Through morphological opening operation and bilateral filtering in the converted HSV space, the edge contour information is enhanced and irrelevant noise interference is filtered out, so that the area positioning of the riding seal is further performed through Hough circle transformation, and binary analysis and pixel statistics after polar coordinate transformation are processed, and finally a threshold score is obtained. The threshold score is compared with a set fixed value, so that the detection result can be effectively and accurately judged.

Description

Technical Field

[0001] This application relates to the fields of industrial vision and energy power technology, specifically to a method and system for detecting and recognizing seals across the seams of power meters. Background Technology

[0002] In recent years, with the development of industrial production and modernization, energy security, especially power supply and safety, has become an issue that cannot be ignored. From top to bottom, from power plants, transmission lines, and substations to end-user power instruments and equipment, all play a vital role.

[0003] In real-world scenarios, manually inspecting the seal area of ​​electrical instrument equipment for abnormalities requires significant manpower and exposes workers to high-voltage risks. Therefore, this application provides a machine vision-based method and system for detecting and recognizing seals across the seams of electrical instruments, enabling rapid and effective detection of any abnormalities in the seals. Summary of the Invention

[0004] This application proposes a method and system for detecting and identifying seals across the seams of power meters to solve the technical problems mentioned in the background section above.

[0005] In a first aspect, embodiments of this application provide a method for detecting and identifying seals across the seams of power meters, characterized by comprising the following steps:

[0006] S110. Convert the original RGB image to an HSV color space image, and preprocess the HSV color space image.

[0007] S120. Perform Hough circle transform on the preprocessed image to obtain a circular fitting set, and sort the circular fitting set according to the number of pixels to obtain the largest circular region.

[0008] S130. Extract the outermost rectangle from the largest circular region to obtain a rectangular image. Convert the rectangular image into a grayscale image, binarize the grayscale image, and normalize the pixels of the binarized image.

[0009] S140. Average all pixels in the normalized image and invert each bit, calculate the number of black pixels, set a fixed threshold to judge the number of black pixels, and finally obtain the recognition result.

[0010] The above technical solution utilizes various preprocessing methods in image processing to obtain images that meet the requirements. Then, Hough circle transform is used to identify the target object region, which has high feasibility for accurately identifying sealed beans and can initially realize the transformation from manual inspection to machine inspection.

[0011] In a specific embodiment, in step S110, the preprocessing of the HSV color space image includes performing morphological opening and bilateral filtering operations on the HSV color space image, followed by Canny edge detection.

[0012] In a specific embodiment, step S120 involves performing a Hough circle transform on the preprocessed image, including the following sub-steps:

[0013] S121. Use the Sobel operator to calculate the gradient of the preprocessed image. Draw line segments along the gradient direction and the opposite direction of the preprocessed image. The starting point and length of the line segments are determined by the set parameters. Count the points that the line segments pass through in the accumulator. The more points are counted, the more likely they are to become the center of the circle.

[0014] S122. Sort all non-zero points by distance from the center of the circle from smallest to largest, starting with the smallest radius and counting them sequentially. Points that differ by a certain amount are considered to be in the same circle. Count all points belonging to the circle. Gradually increase the radius and continue counting. Compare the line density of two radii: number of points / radius. The higher the line density, the greater the reliability of the radius. Repeat steps S121 and S122 within the parameter allowable range until the optimal radius is obtained.

[0015] In a specific embodiment, in step S130, the outermost rectangle is cropped according to the largest circular region to obtain a rectangular image. The rectangular image is converted into a grayscale image, and the grayscale image is subjected to threshold binarization. At the same time, the numerical parameters of binarization are adjusted. The polar coordinate transformation of the image is implemented based on OpenCV. Finally, the pixels of the image after polar coordinate transformation are normalized.

[0016] In a specific embodiment, polar coordinate transformation of the image is implemented based on OpenCV to convert the sector region into a rectangular region, and nearest neighbor interpolation is performed based on the width and height of the rectangular region.

[0017] In a specific embodiment, step S130 involves normalizing the pixels of the image after polar coordinate transformation, including the following sub-steps:

[0018] S131. Normalize the grayscale gradient magnitude of the image after polar coordinate transformation; and

[0019] S132. Using the gray-level gradient calculation method in Canny edge detection, obtain the gray-level gradient magnitude image M(x,y) of the gray-level image I(x,y), and normalize M(x,y) to scale, as follows:

[0020]

[0021] Where Ms(x,y) is the normalized image gradient magnitude image, and (x,y) represents the pixel coordinates of the image. This represents the maximum value of the grayscale gradient amplitude image; scale represents the maximum range value set according to design requirements.

[0022] In a specific embodiment, in step S140, a fixed threshold is set to determine the number of black pixels, and the final recognition result is obtained, including: if the number of black pixels is within the set fixed threshold, the output result is True; otherwise, the output result is False.

[0023] In a specific embodiment, in step S120, a circular fitting set is obtained, and the circular fitting set is sorted according to the number of pixels. Specifically, the circles in the circular fitting set are sorted from largest to smallest by area calculation and comparison, and finally the largest circular area is obtained.

[0024] Secondly, this application provides a system for detecting and identifying seals across the seams of electrical instruments, the system comprising:

[0025] The preprocessing module is used to convert the original RGB image into an HSV color space image and to preprocess the HSV color space image.

[0026] The Hough circle transform module is used to perform Hough circle transform on the preprocessed image to obtain a set of circular fittings, and sort the set of circular fittings according to the number of pixels to obtain the largest circular region.

[0027] The conversion module is used to extract the outermost rectangle from the largest circular region to obtain a rectangular image, convert the rectangular image into a grayscale image, perform binarization on the grayscale image, and normalize the pixels of the binarized image; and

[0028] The output module is used to average all pixels in the normalized image and invert each bit, calculate the number of black pixels, set a fixed threshold to judge the number of black pixels, and finally obtain the recognition result.

[0029] Thirdly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements any of the methods described above.

[0030] This application provides a method and system for detecting and recognizing seals across the seams of power meters. The system includes image processing methods such as locating the target area of ​​the seal and the stamp on the power meter equipment, Hough circle transformation, and polar coordinate transformation. Morphological opening and bilateral filtering are performed in the transformed HSV space to enhance edge contour information and filter out irrelevant noise interference. Furthermore, Hough circle transformation is used to locate the area of ​​the seal, and binary analysis and pixel statistics are performed after polar coordinate transformation. Finally, a threshold score is obtained. By comparing this threshold score with a set fixed value, the final output detection result can be determined. Attached Figure Description

[0031] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0032] Figure 1 This is a flowchart of a method for detecting and identifying cross-stitch seals for power instrument inspection according to this application;

[0033] Figure 2 This is a schematic flowchart of a method for detecting and identifying cross-stitch seals for power instrument inspection according to this application.

[0034] Figure 3 This is a schematic diagram of the original RGB image according to an embodiment of this application;

[0035] Figure 4 This is a schematic diagram of an HSV color space image according to an embodiment of this application;

[0036] Figure 5 This is a schematic diagram of an opening-HSV color space image according to an embodiment of this application;

[0037] Figure 6 This is a schematic diagram of a bilaterally filtered HSV color space image according to an embodiment of this application;

[0038] Figure 7 This is a schematic diagram of a Canny edge detection image according to an embodiment of this application;

[0039] Figure 8 This is a schematic diagram of a Hough circle detection image according to an embodiment of this application;

[0040] Figure 9This is a schematic diagram of a cropped region image according to an embodiment of this application;

[0041] Figure 10 This is a schematic diagram of a binarized image according to an embodiment of this application;

[0042] Figure 11 This is a schematic diagram of the polar coordinate transformation result according to an embodiment of this application;

[0043] Figure 12 This is a schematic diagram of mean normalization according to an embodiment of this application;

[0044] Figure 13 This is a schematic diagram of a cross-stitch detection and identification system for power instrument inspection according to this application;

[0045] Figure 14 This is a schematic diagram of the structure of a computer system suitable for implementing the electronic devices of the present application embodiments. Detailed Implementation

[0046] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0047] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0048] Figure 1 A flowchart of a method for detecting and identifying seals across the seams of electrical instruments according to this application is shown. Figure 2 A schematic flowchart of a method for detecting and identifying seals across the seams of electrical instruments according to this application is shown; in conjunction with reference to... Figure 1 and Figure 2 The method 100 includes the following steps:

[0049] S110. Convert the original RGB image to an HSV color space image, and preprocess the HSV color space image.

[0050] In this embodiment, preprocessing the HSV color space image includes performing morphological opening and bilateral filtering operations on the HSV color space image, followed by Canny edge detection. (Refer to the reference...) Figures 3-7 , Figures 3-7The diagrams shown are schematic representations of an original RGB image, an HSV color space image, an opening-HSV color space image, a bilaterally filtered-HSV color space image, and a Canny edge detection image, respectively, according to an embodiment of this application.

[0051] S120. Perform a Hough circle transform on the preprocessed image to obtain a set of circular fittings. Sort the set of circular fittings according to the number of pixels and obtain the largest circular region. The circles in the set can be sorted from largest to smallest by area calculation and comparison to finally obtain the largest circular region. Please refer to [reference needed]. Figure 8 , Figure 8 A schematic diagram of a Hough circle detection image according to an embodiment of this application is shown.

[0052] In this embodiment, the preprocessed image is subjected to Hough circle transform, which includes the following sub-steps:

[0053] S121. Use the Sobel operator to calculate the gradient of the preprocessed image. Draw line segments along the gradient direction and the opposite direction of the preprocessed image. The starting point and length of the line segments are determined by the set parameters. Count the points that the line segments pass through in the accumulator. The more points are counted, the more likely they are to become the center of the circle.

[0054] S122. Sort all non-zero points by distance from the center of the circle from smallest to largest. Start counting from the smallest radius. Points that differ by a certain amount are considered to be in the same circle. Count all points belonging to the circle. Gradually increase the radius and continue counting. Compare the line density of two radii: number of points / radius. The higher the line density, the greater the reliability of the radius. Repeat steps S121 and S122 within the parameter allowable range until the optimal radius is obtained.

[0055] S130. Extract the outermost rectangle from the largest circular region to obtain a rectangular image. Convert the rectangular image to a grayscale image, perform binarization on the grayscale image, and normalize the pixels of the binarized image. Please refer to the reference. Figures 9-12 , Figures 9-12 These are schematic diagrams of a cropped region image, a binarized image, a polar coordinate transformation result, mean value, and normalization, respectively, according to an embodiment of this application.

[0056] In this embodiment, the outermost rectangle is cropped from the largest circular region to obtain a rectangular image. The rectangular image is then converted into a grayscale image. The grayscale image is then binarized using the threshold method, and the binarization parameters are adjusted. Polar coordinate transformation of the image is implemented using OpenCV to convert the sector region into a rectangular region. Nearest neighbor interpolation is performed based on the width and height of the rectangular region. Finally, the pixels of the image after polar coordinate transformation are normalized.

[0057] In this embodiment, the pixels of the image after polar coordinate transformation are finally normalized, including the following sub-steps:

[0058] S131. Normalize the grayscale gradient magnitude of the image after polar coordinate transformation; and

[0059] S132. Using the gray-level gradient calculation method in Canny edge detection, obtain the gray-level gradient magnitude image M(x,y) of the gray-level image I(x,y), and normalize M(x,y) to scale, as follows:

[0060]

[0061] Where Ms(x,y) is the normalized image gradient magnitude image, and (x,y) represents the pixel coordinates of the image. This represents the maximum value of the grayscale gradient amplitude image; scale represents the maximum range value set according to design requirements.

[0062] S140. Average all pixels in the normalized image and invert each bit, calculate the number of black pixels, set a fixed threshold to judge the number of black pixels, and finally obtain the recognition result.

[0063] In this embodiment, a fixed threshold is set to judge the number of black pixels, and the final recognition result is obtained. If the number of black pixels is within the set fixed threshold, the output result is True, that is, the seal bean on the seam is normal; otherwise, the output result is False, that is, the seal bean is abnormal.

[0064] Further reference Figure 13 As an implementation of the above-described method, this application provides an embodiment of a system for detecting and identifying seals across the seams of power meters for inspection. This system embodiment is similar to... Figure 1 Corresponding to the illustrated method embodiment, this system can be specifically applied to various electronic devices. The system 200 includes:

[0065] Preprocessing module 210 is used to convert the original RGB image into an HSV color space image and to preprocess the HSV color space image.

[0066] The Hough circle transform module 220 is used to perform Hough circle transform on the preprocessed image to obtain a circular fitting set, and sort the circular fitting set according to the number of pixels to obtain the largest circular region.

[0067] The conversion module 230 is used to extract the outermost rectangle based on the largest circular region to obtain a rectangular image, convert the rectangular image into a grayscale image, perform binarization on the grayscale image, and normalize the pixels of the binarized image; and

[0068] The output module 240 is used to average and invert all pixels in the normalized image, calculate the number of black pixels, set a fixed threshold to judge the number of black pixels, and finally obtain the recognition result.

[0069] like Figure 14 As shown, the computer system 300 includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 302 or programs loaded from storage section 308 into random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the operation of the system 300. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0070] The following components are connected to I / O interface 305: an input section 306 including a keyboard, mouse, etc.; an output section 307 including a liquid crystal display (LCD) and speakers, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN card and a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to I / O interface 305 as needed. A removable medium 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 310 as needed so that computer programs read from it can be installed into storage section 308 as needed.

[0071] Specifically, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the functions defined in the methods of this application. It should be noted that the computer-readable medium described in this application can be a computer-readable signal medium or a computer-readable medium or any combination thereof. The computer-readable medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, a computer-readable medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. Program code contained on a computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0072] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0073] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0074] The modules described in the embodiments of this application can be implemented in software or hardware. The described modules can also be housed in a processor; for example, a processor can be described as including an acquisition module, an analysis module, and an output module. The names of these modules do not necessarily limit the functionality of the module itself.

[0075] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A method for detecting and identifying a rider's stamp for electric power meter inspection, characterized by, Includes the following steps: S110. Convert the original RGB image to an HSV color space image, and preprocess the HSV color space image. S120. Perform Hough circle transform on the preprocessed image to obtain a set of circular fittings, and sort the set of circular fittings according to the number of pixels to obtain the largest circular region. The Hough circle transform includes the following sub-steps: S121. Use the Sobel operator to calculate the gradient of the preprocessed image. Draw line segments along the gradient direction and the opposite direction of the preprocessed image. The starting point and length of the line segments are determined by the set parameters. Count the points that the line segments pass through in the accumulator. The more points are counted, the more likely they are to become the center of the circle. S122. Sort all non-zero points by distance from the center of the circle from smallest to largest, starting with the smallest radius and counting them sequentially. Points that differ by a certain amount are considered to be in the same circle. Count all points belonging to the circle. Gradually increase the radius and continue counting. Compare the line density of two radii: number of points / radius. The higher the line density, the greater the reliability of the radius. Repeat steps S121 and S122 within the parameter allowable range until the optimal radius is obtained. S130. Extract the outermost rectangle from the largest circular region to obtain a rectangular image. Convert the rectangular image into a grayscale image. Perform threshold binarization on the grayscale image and adjust the numerical parameters of the binarization. Implement polar coordinate transformation of the image based on OpenCV. Finally, normalize the pixels of the image after polar coordinate transformation. as well as S140. Average all pixels in the normalized image and invert each bit, calculate the number of black pixels, set a fixed threshold to judge the number of black pixels, and finally obtain the recognition result.

2. A method for detecting and identifying a riding stamp on an electric power meter according to claim 1, wherein In step S110, the preprocessing of the HSV color space image includes performing morphological opening and bilateral filtering operations on the HSV color space image, followed by Canny edge detection.

3. A method for detecting and identifying a riding stamp on an electric meter according to claim 1, wherein In step 130, the image polar coordinate transformation is implemented based on OpenCV to convert the sector region into a rectangular region, and nearest neighbor interpolation is performed based on the width and height of the rectangular region.

4. The method for detecting and identifying the riding stamp of the electric power meter according to claim 1, characterized in that, In step S130, the pixels of the image after polar coordinate transformation are finally normalized, including the following sub-steps: S131. Normalize the grayscale gradient magnitude of the image after polar coordinate transformation; and S132. Using the gray-level gradient calculation method in Canny edge detection, obtain the gray-level gradient magnitude image M(x,y) of the gray-level image I(x,y), and normalize M(x,y) to scale, as follows: wherein Ms(x, y) is a normalized image gradient magnitude image, (x, y) represents pixel coordinates of the image, represents a maximum value of the gray scale gradient magnitude image; scale represents a maximum range value set according to design requirements.

5. A method for detecting and identifying a rider's stamp for inspection of an electric power meter according to claim 1, characterized in that, In step S140, a fixed threshold is set to determine the number of black pixels, and the final recognition result is obtained, including: if the number of black pixels is within the set fixed threshold, the output result is True; otherwise, the output result is False.

6. A method for detecting and identifying a rider's stamp for inspection of an electric power meter according to claim 1, characterized in that, In step S120, a circular fitting set is obtained, and the circular fitting set is sorted according to the number of pixels. Specifically, the circles in the circular fitting set are sorted from largest to smallest by area calculation and comparison, and finally the largest circular area is obtained.

7. A system for electric meter verification, characterized by, The system includes: The preprocessing module is used to convert the original RGB image into an HSV color space image and to preprocess the HSV color space image. The Hough circle transform module is used to perform a Hough circle transform on the preprocessed image to obtain a set of circular fittings. The set of circular fittings is then sorted according to the number of pixels to obtain the largest circular region. The Hough circle transform includes the following sub-steps: The Sobel operator is used to calculate the gradient of the preprocessed image. Line segments are drawn along the gradient direction and the opposite direction of the preprocessed image. The starting point and length of the line segments are determined by the set parameters. The points passed by the line segments are counted in the accumulator. The more points are counted, the more likely they are to become the center of the circle. Sort all non-zero points by distance from the center of the circle from smallest to largest, starting with the smallest radius and counting them sequentially. Points that differ by a certain amount are considered to be in the same circle. Count all points belonging to the circle. Gradually increase the radius and continue counting. Compare the line density of two radii: number of points / radius. The higher the line density, the greater the reliability of the radius. Repeat steps S121 and S122 within the parameter allowable range until the optimal radius is obtained. The conversion module is used to extract the outermost rectangle from the largest circular region to obtain a rectangular image, convert the rectangular image into a grayscale image, perform threshold binarization on the grayscale image, adjust the numerical parameters of the binarization, implement polar coordinate transformation of the image based on OpenCV, and finally normalize the pixels of the image after polar coordinate transformation; and The output module is used to average all pixels in the normalized image and invert each bit, calculate the number of black pixels, set a fixed threshold to judge the number of black pixels, and finally obtain the recognition result.

8. A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method as described in any one of claims 1-6.

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