Rapid detection method and system for distribution box components

Abstract

The present application relates to the field of image enhancement technology, and specifically to a rapid detection method and system for distribution box components. The method includes: obtaining the grayscale change characteristic value of the reference pixel according to the difference between the grayscale value of the reference pixel and the grayscale value of all pixels in the fixed window of the reference pixel; obtaining the significance value of the reference pixel according to the average grayscale value of the fixed window of the reference pixel, the average grayscale value of the sliding window, the correlation between the fixed window and the sliding window of the reference pixel, and the grayscale change characteristic value of the reference pixel; obtaining the enhancement degree of different areas in the grayscale image according to the average significance value of the pixels in the cluster, the average grayscale value of the pixels in the cluster, the minimum grayscale value of the pixels, and the slope of the pixels in the cluster; according to the enhanced image, the line defects of the flexible circuit board can be identified, and the line detection of the flexible circuit board can be accurately completed.

Description

Technical Field

[0001] The present application relates to the field of image enhancement technology, and in particular to a rapid detection method and system for distribution box components. Background Art

[0002] In modern power distribution systems, distribution boxes are an important part of power management, used to control and distribute power. In order to improve the functionality, reliability and space utilization of distribution boxes, more and more distribution box designs have introduced flexible circuit boards as internal components. The main advantages of flexible circuit boards include their lightness, flexibility and high integration, which make their application inside distribution boxes have significant advantages. Because flexible circuit boards are easy to bend and fold, traditional detection methods cannot accurately detect their functions and performance. In response to the detection needs of flexible circuit boards, some detection technologies have emerged in recent years, such as flexible probe detection, optical imaging detection, capacitive detection, etc. These technologies can better adapt to the characteristics of flexible circuit boards and achieve fast and accurate detection. Optical imaging detection uses optical imaging technology to detect and image flexible circuit boards, usually using equipment such as microscopes and infrared cameras. By observing the surface and internal structure of the flexible circuit board, its wiring irregularities, burrs, welding problems, etc. can be detected, and its quality and performance can be evaluated. However, because the flexible circuit board components of the distribution box have dense lines, when enhancing the image of the flexible circuit board, there may be problems with local contrast non-uniformity, resulting in the inability to accurately identify line defects, which in turn affects the detection results of the flexible circuit board. Summary of the invention

[0003] The present application provides a rapid detection method and system for distribution box components to solve the problem that the flexible circuit board of the distribution box component has uneven local contrast and cannot accurately identify line defects, resulting in inaccurate flexible circuit board detection results.

[0004] The rapid detection method and system for distribution box components of the present application adopt the following technical solutions:

[0005] An embodiment of the present application provides a rapid detection method for a distribution box assembly, the method comprising the following steps:

[0006] Obtain a grayscale image of the flexible circuit board;

[0007] The grayscale image Pixels are reference pixels;

[0008] Obtaining a grayscale change characteristic value of the reference pixel according to the difference between the grayscale value of the reference pixel and the grayscale values ​​of all pixels in a fixed window of the reference pixel;

[0009] Obtaining the correlation between the fixed window of the reference pixel and the sliding window according to the minimum grayscale value of the pixel in the sliding window, the maximum grayscale value of the pixel in the fixed window of the reference pixel, the maximum grayscale value of the pixel in the sliding window, and the minimum grayscale value of the pixel in the fixed window of the reference pixel;

[0010] Obtaining a significance value of a reference pixel according to an average grayscale value of a fixed window of the reference pixel, an average grayscale value of a sliding window, a correlation between the fixed window and the sliding window of the reference pixel, and a grayscale change characteristic value of the reference pixel;

[0011] According to the significance value of each pixel, the pixels are clustered to obtain several clusters;

[0012] According to the average significance value of the pixels in the clusters, the average gray value of the pixels in the clusters, the minimum gray value of the pixels, and the slope of the pixels in the clusters, the enhancement degree of different areas in the gray image is obtained;

[0013] Perform image enhancement according to the enhancement degree of different regions in the grayscale image to obtain an enhanced image;

[0014] Based on the enhanced image, defects of the flexible circuit board are identified.

[0015] Preferably, the grayscale change characteristic value of the reference pixel is obtained according to the difference between the grayscale value of the reference pixel and the grayscale values ​​of all pixels in the fixed window of the reference pixel, and the specific formula included is as follows:

[0016]

[0017] In the formula, Represents the grayscale change characteristic value of the reference pixel, Represents the gray value of the reference pixel. The first pixel in the fixed window representing the reference pixel The gray value of a pixel, Represents the number of pixels in a fixed window.

[0018] Preferably, the correlation between the fixed window of the reference pixel and the sliding window is obtained according to the minimum grayscale value of the pixel in the sliding window, the maximum grayscale value of the pixel in the fixed window of the reference pixel, the maximum grayscale value of the pixel in the sliding window, and the minimum grayscale value of the pixel in the fixed window of the reference pixel, and the specific formula included is as follows:

[0019]

[0020] In the formula, Represents the fixed window of the reference pixel and the The correlation between sliding windows, Indicates The minimum gray value of the pixel in the sliding window. Represents the maximum grayscale value of the pixel in the fixed window of the reference pixel. Indicates The maximum gray value of the pixel in the sliding window is Represents the minimum grayscale value of the pixel in the fixed window of the reference pixel.

[0021] Preferably, the significance value of the reference pixel is obtained according to the average gray value of the fixed window of the reference pixel, the average gray value of the sliding window, the correlation between the fixed window and the sliding window of the reference pixel, and the grayscale change characteristic value of the reference pixel, and the specific formula included is as follows:

[0022]

[0023] In the formula, Represents the saliency value of the reference pixel, Represents the average grayscale value of a fixed window of reference pixels, Indicates The average gray value of the sliding window, Indicates the number of sliding windows; Represents the grayscale change characteristic value of the reference pixel; Represents the fixed window of the reference pixel and the The correlation between sliding windows.

[0024] Preferably, clustering the pixels according to the significance value of each pixel to obtain a plurality of clusters includes the following specific steps:

[0025] The row c, column d and corresponding significance value f of each pixel point constitute a three-dimensional data point (c, d, f); all pixels in the grayscale image correspond to a three-dimensional data point respectively, and all data points are clustered using the DBSCAN clustering algorithm to obtain several clusters.

[0026] Preferably, the step of obtaining the enhancement degree of different regions in the grayscale image according to the average significance value of the pixels in the clusters, the average grayscale value of the pixels in the clusters, the minimum grayscale value of the pixels, and the slope of the pixels in the clusters comprises the following specific steps:

[0027] According to the slope difference between each pixel and its adjacent pixels in each cluster, the slope feature of each cluster is obtained;

[0028] No. The calculation formula for the enhancement degree of a cluster is as follows:

[0029]

[0030] In the formula, Indicates The enhancement degree of clusters, Indicates The average significance value of the pixels in the clusters; Indicates The slope characteristics of the clusters; represents the linear normalization function;

[0031] The enhancement degree of the area corresponding to each cluster in the grayscale image is equal to the enhancement degree of each cluster.

[0032] Preferably, the slope feature of each cluster is obtained according to the slope difference between each pixel point and adjacent pixel points in each cluster, and the specific formula included is as follows:

[0033]

[0034] In the formula, Indicates The slope characteristics of the clusters; Indicates The average gray value of the pixels in the clusters, Indicates the minimum gray value of all pixels; Indicates The number of edge pixels in the area corresponding to each cluster; Indicates The i-th pixel point on the edge of the area corresponding to the cluster, the The absolute value of the slope difference between pixels.

[0035] Preferably, the image enhancement is performed according to the enhancement degree of different regions in the grayscale image to obtain the enhanced image, and the specific steps include the following:

[0036] According to the enhancement degree of different regions in the grayscale image, an adaptive contrast enhancement algorithm is used to enhance each region on the grayscale image to obtain an enhanced image.

[0037] Preferably, the defect identification of the flexible circuit board according to the enhanced image includes the following specific steps:

[0038] The template matching algorithm is used to obtain abnormal matching points on the enhanced image, and the abnormal matching points are used as defective areas in the flexible circuit board.

[0039] The present application also proposes a rapid detection system for distribution box components, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned rapid detection method for distribution box components are implemented.

[0040] The beneficial effects of the technical solution of this application are:

[0041] The present application obtains the correlation between the fixed window and the sliding window based on the difference in the grayscale values ​​of the pixels between the fixed window and the sliding window, and obtains the significance value of the pixel based on the correlation between the fixed window and all the sliding windows. The correlation is used to characterize the similarity of the grayscale distribution between the local area of ​​the flexible circuit board and other areas, wherein the grayscale distribution between the local area containing defects and the local area not containing defects and other areas is obviously different, so the significance value obtained by the correlation can be used to accurately characterize the distribution of defects on the entire flexible circuit board; further, the significance value of each pixel is obtained based on the correlation, and the pixels are clustered based on the significance value, the pixels with the same defect characteristics are clustered together, and the grayscale image is divided into multiple areas, thereby obtaining the degree of enhancement of different areas; the degree of enhancement of different areas can better adapt to the local characteristics of the grayscale image, better distinguish the grayscale contrast of different areas, and adaptively enhance different areas based on the degree of enhancement, thereby improving the enhancement effect, avoiding the situation where the local contrast is uneven and the line defects cannot be accurately identified, and the detection of flexible circuit boards in distribution boxes can be completed accurately and quickly. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 A flowchart of the steps of a rapid detection method for distribution box components provided in an embodiment of the present application. DETAILED DESCRIPTION

[0044] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the rapid detection method and system for distribution box components proposed in the present application, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0045] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0046] The specific scheme of the rapid detection method and system for distribution box components provided by the present application is described in detail below with reference to the accompanying drawings.

[0047] See also Figure 1 , which shows a flowchart of a method for rapid detection of a distribution box assembly provided by an embodiment of the present application, the method comprising the following steps:

[0048] Step S001: obtaining a grayscale image of a flexible circuit board.

[0049] It should be noted that the power module in the distribution box usually needs to transmit power to various electronic devices. The flexible circuit board can be used as part of the power line to transmit power to the devices that need power. Since the flexible circuit board has good bending performance and can adapt to different wiring requirements, it can transmit power more flexibly.

[0050] First, the image of the flexible circuit board is collected. A high-definition CCD camera is installed above the experimental platform to shoot the surface image of the component flexible circuit board of the distribution box vertically from above. The background of the experimental platform is uniform and has no foreign colors. Then the collected flexible circuit board image is transmitted to the image processing system through a data cable. Because when collecting the image of the flexible circuit board, the collected flexible circuit board image may contain noise due to ambient light or sensor heating, and the obtained flexible circuit board image needs to be denoised. This embodiment uses a non-local mean filtering algorithm to denoise the obtained flexible circuit board image to obtain a denoised flexible circuit board image. The non-local mean filtering algorithm is a well-known technology, and this embodiment will not repeat the specific implementation process. The denoised flexible circuit board image is then grayed to obtain a grayscale image.

[0051] Step S002: Obtain the grayscale change characteristic value of the reference pixel based on the difference between the grayscale value of the reference pixel and the grayscale values ​​of all pixels in the fixed window of the reference pixel; obtain the significance value of the reference pixel based on the average grayscale value of the fixed window of the reference pixel, the average grayscale value of the sliding window, the correlation between the fixed window and the sliding window of the reference pixel, and the grayscale change characteristic value of the reference pixel.

[0052] It should be noted that the flexible circuit board of the distribution box is thinner and lighter than the traditional rigid circuit board. It has a high-density wiring capability and can realize complex circuit design in a smaller space, thereby improving the integration and performance of electronic products. It is precisely because of its high wiring density that there may be local contrast unevenness when the original image is enhanced. In the image enhancement process, if this unevenness is not effectively handled, some areas may be over-enhanced and other areas may be under-enhanced, making the overall image unbalanced. In addition, there may be subtle structures and details on the flexible circuit board, such as tiny lines, solder joints, etc. In the enhancement process, it is necessary to ensure that these subtle structures can be effectively retained rather than being overly blurred or lost.

[0053] The grayscale image The pixel point is taken as the reference pixel point. In order to better preserve the image details when enhancing the grayscale image, this implementation sets two windows, one of which is centered on the reference pixel point. The other is a fixed window of size A sliding window is used, starting from the upper left corner of the grayscale image and sliding from left to right and from top to bottom. The sliding step size is set to 1 in this embodiment. The pixels of the sliding window do not overlap. When the window size is not satisfied when sliding to the right boundary, the redundant pixels are discarded. Then, the significance value of the reference pixel is obtained by comparing the grayscale difference between the sliding window and the fixed window. The above window size and sliding step size are all empirical values, which can be set by the implementer according to different implementation environments. This application does not impose any special restrictions on this.

[0054] As an example, the calculation formula is as follows:

[0055]

[0056] In formula (1), Represents the grayscale change characteristic value of the reference pixel, Represents the gray value of the reference pixel. The first pixel in the fixed window representing the reference pixel The gray value of a pixel, Represents the number of pixels in a fixed window. Indicates the first pixel in the fixed window between the reference pixel and the reference pixel. The greater the difference in the grayscale values ​​of the reference pixels, the more obvious the grayscale change of the reference pixel is.

[0057] In formula (2), Represents the fixed window of the reference pixel and the The correlation between sliding windows, Indicates The minimum gray value of the pixel in the sliding window. Represents the maximum grayscale value of the pixel in the fixed window of the reference pixel. Indicates The maximum gray value of the pixel in the sliding window is Represents the minimum grayscale value of the pixel in the fixed window of the reference pixel. The smaller the gray value, the greater the correlation, because the gray value of the circuit board line is smaller. Therefore, when the gray value of the area is smaller, the possibility that it is a circuit board line is greater, and therefore the correlation is greater. Represents the fixed window of the reference pixel and the The correlation of the grayscale distribution of pixels in the sliding window. The smaller the difference of the maximum value and the minimum value, the greater the correlation. Therefore, we use This function model changes the monotonicity, Represents the independent variable function; the closer the ratio is to 0, the greater the correlation.

[0058] In formula (3), Represents the saliency value of the reference pixel, Represents the average grayscale value of a fixed window of reference pixels, Indicates The average gray value of the sliding window, Indicates the number of sliding windows; Represents the grayscale change characteristic value of the reference pixel; Represents the fixed window of the reference pixel and the The correlation between sliding windows. The saliency weight of each sliding window relative to the fixed window is then multiplied by the grayscale change feature value of the reference pixel to represent the feature value of the reference pixel relative to each sliding window, and then summed to obtain the saliency value of the reference pixel. According to the above example calculation process, the grayscale change feature value of each pixel and the saliency value of each pixel can be obtained.

[0059] At this point, the saliency value of each pixel is obtained.

[0060] Step S003: Cluster the pixels according to the significance value of each pixel to obtain several clusters; obtain the degree of enhancement of different areas in the grayscale image according to the average significance value of the pixels in the clusters, the average grayscale value of the pixels in the clusters, the minimum grayscale value of the pixels, and the slope of the pixels in the clusters; perform image enhancement according to the degree of enhancement of different areas in the grayscale image to obtain an enhanced image.

[0061] According to the saliency value of each pixel point obtained above, the continuity of the pixel points is then analyzed to obtain the enhancement degree of different areas.

[0062] It should be noted that since the wiring of the flexible circuit board is relatively dense, when performing image enhancement processing on it, it is necessary to obtain clear lines so that the line area will not appear blurred after image enhancement. And the lines are all regular long strips, so different areas are enhanced according to the characteristics of the grayscale image.

[0063] First, all pixels are clustered according to the obtained significance value of each pixel; the row c, column d and corresponding significance value f of each pixel constitute a three-dimensional data point (c, d, f). All pixels in the grayscale image correspond to a three-dimensional data point. The DBSCAN density clustering algorithm is used to cluster these data points. The cluster radius is set to 5 and the minimum number of clusters is set to 5. The above values ​​are empirical values ​​and can be set by the implementer according to different implementation environments. After clustering, several clusters are obtained. The pixels in each cluster constitute an area. The edge of each cluster corresponding to the area is obtained by the canny edge detection algorithm; the slope of the two adjacent nearest pixels on the edge of the cluster corresponding to the area is obtained. Among them, canny edge detection and slope calculation are well-known technologies, and the specific process will not be repeated. Then, the degree of enhancement of different areas is obtained according to the shape characteristics and grayscale differences of each cluster.

[0064]

[0065]

[0066] In the formula, represents the slope characteristic of the rth cluster, Indicates The average gray value of the pixels in the clusters, Indicates the minimum gray value of all pixels; Indicates The number of edge pixels in the area formed by the clusters; Indicates The i-th pixel point on the edge of the area corresponding to the cluster, the The absolute value of the slope difference between pixels;

[0067] Indicates The enhancement degree of clusters, Indicates The average significance value of the pixels in the clusters, represents the linear normalization function.

[0068] It should be noted that Indicates The difference between the average grayscale value of the clusters and the minimum grayscale value in the grayscale image is because in the flexible circuit board image, the circuit is etched on the substrate, and the grayscale of the etched circuit is smaller than the grayscale of the substrate area. Therefore, the smaller the grayscale, the more likely it is the circuit area. Therefore, the smaller the difference, the greater the degree of enhancement, so as to expand the degree of enhancement of the circuit area. It represents the difference in slopes of adjacent pixels on the edge of the area formed by the pixels in the rth cluster. Since the lines on the flexible circuit board in the distribution box are basically straight lines, the smaller the difference in slopes between the two adjacent pixels on the edge of the cluster, the more regular the cluster is, and the more likely the area formed is a line area, so the greater the degree of enhancement.

[0069] Furthermore, the enhancement degree of each cluster is obtained according to the above steps, and the enhancement degree of each cluster is used as the enhancement degree of the region corresponding to each cluster in the grayscale image.

[0070] According to the enhancement degree of different regions obtained above, the enhancement degree of different regions is used as the contrast gain parameter of the adaptive contrast enhancement (ACE) algorithm, and then the grayscale image is enhanced to finally obtain the enhanced image. The adaptive contrast enhancement (ACE) algorithm is a well-known technology, and the specific implementation process is not repeated in this embodiment.

[0071] At this point, the enhanced image is obtained.

[0072] Step S004: performing defect identification on the flexible circuit board according to the enhanced image.

[0073] According to the enhanced image, the defects of the flexible circuit board are identified. This embodiment uses a template matching algorithm to detect defects. The template matching algorithm is a well-known technology, and the specific implementation process is not repeated in this embodiment.

[0074] First, a template circuit board that is the same as the test circuit board is obtained. The template circuit board is manually selected and has no quality defects. Then, the enhanced image is matched with the grayscale image of the template circuit board to obtain abnormal matching points. The abnormal matching points are the areas where defects exist in the flexible circuit board. Then, they are marked and the defective areas are obtained based on the marked pixels using the connected domain extraction method, thereby completing the line detection of the component flexible circuit board of the distribution box. In this embodiment, when the area of ​​the defective area is greater than 0.1% of the area of ​​the enhanced image, the detection result is set to unqualified.

[0075] The present application is based on the same inventive concept as the above-mentioned method. An embodiment of the present application also provides a rapid detection system for distribution box components, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above-mentioned rapid detection methods for distribution box components are implemented.

[0076] It should be noted that the above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

[0077] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0078] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application should be included in the protection scope of the present application.

Claims

1. A rapid detection method for distribution box components, characterized in that: The method comprises the following steps: Obtain a grayscale image of the flexible circuit board; The grayscale image Pixels are reference pixels; Obtaining a grayscale change characteristic value of the reference pixel according to the difference between the grayscale value of the reference pixel and the grayscale values ​​of all pixels in a fixed window of the reference pixel; Obtaining the correlation between the fixed window of the reference pixel and the sliding window according to the minimum grayscale value of the pixel in the sliding window, the maximum grayscale value of the pixel in the fixed window of the reference pixel, the maximum grayscale value of the pixel in the sliding window, and the minimum grayscale value of the pixel in the fixed window of the reference pixel; Obtaining a significance value of a reference pixel according to an average grayscale value of a fixed window of the reference pixel, an average grayscale value of a sliding window, a correlation between the fixed window and the sliding window of the reference pixel, and a grayscale change characteristic value of the reference pixel; According to the significance value of each pixel, the pixels are clustered to obtain several clusters; According to the average significance value of the pixels in the clusters, the average gray value of the pixels in the clusters, the minimum gray value of the pixels, and the slope of the pixels in the clusters, the enhancement degree of different areas in the gray image is obtained; Perform image enhancement according to the enhancement degree of different regions in the grayscale image to obtain an enhanced image; According to the enhanced image, defects of the flexible circuit board are identified; The method of obtaining the enhancement degree of different regions in the grayscale image according to the average significance value of the pixels in the clusters, the average grayscale value of the pixels in the clusters, the minimum grayscale value of the pixels, and the slope of the pixels in the clusters includes the following specific steps: According to the slope difference between each pixel and its adjacent pixels in each cluster, the slope feature of each cluster is obtained; No. The calculation formula for the enhancement degree of a cluster is as follows: In the formula, Indicates The enhancement degree of clusters, Indicates The average significance value of the pixels in the clusters; Indicates The slope characteristics of the clusters; represents the linear normalization function; The enhancement degree of the area corresponding to each cluster in the grayscale image is equal to the enhancement degree of each cluster.

2. The rapid detection method for distribution box components according to claim 1 is characterized in that: The grayscale change characteristic value of the reference pixel is obtained according to the difference between the grayscale value of the reference pixel and the grayscale values ​​of all pixels in the fixed window of the reference pixel, and the specific formula included is as follows: In the formula, Represents the grayscale change characteristic value of the reference pixel, Represents the gray value of the reference pixel. The first pixel in the fixed window representing the reference pixel The gray value of a pixel, Represents the number of pixels in a fixed window.

3. The rapid detection method for distribution box components according to claim 1 is characterized in that: The correlation between the fixed window of the reference pixel and the sliding window is obtained according to the minimum gray value of the pixel in the sliding window, the maximum gray value of the pixel in the fixed window of the reference pixel, the maximum gray value of the pixel in the sliding window, and the minimum gray value of the pixel in the fixed window of the reference pixel, including the specific formula as follows: In the formula, Represents the fixed window of the reference pixel and the The correlation between sliding windows, Indicates The minimum gray value of the pixel in the sliding window. Represents the maximum grayscale value of the pixel in the fixed window of the reference pixel. Indicates The maximum gray value of the pixel in the sliding window is Represents the minimum grayscale value of the pixel in the fixed window of the reference pixel.

4. The rapid detection method for distribution box components according to claim 1 is characterized in that: The specific formula for obtaining the significance value of the reference pixel is as follows: In the formula, Represents the saliency value of the reference pixel, Represents the average grayscale value of a fixed window of reference pixels, Indicates The average gray value of the sliding window, Indicates the number of sliding windows; Represents the grayscale change characteristic value of the reference pixel; Represents the fixed window of the reference pixel and the The correlation between sliding windows.

5. The rapid detection method for distribution box components according to claim 1 is characterized in that: The pixel points are clustered according to the significance value of each pixel point to obtain a number of clusters, and the specific steps include the following: The row c, column d and corresponding significance value f of each pixel point constitute a three-dimensional data point (c, d, f); all pixels in the grayscale image correspond to a three-dimensional data point respectively, and all data points are clustered using the DBSCAN clustering algorithm to obtain several clusters.

6. The rapid detection method for distribution box components according to claim 1 is characterized in that: The slope feature of each cluster is obtained according to the slope difference between each pixel point and the adjacent pixel points in each cluster, and the specific formula included is as follows: In the formula, Indicates The slope characteristics of the clusters; Indicates The average gray value of the pixels in the clusters, Indicates the minimum gray value of all pixels; Indicates The number of edge pixels in the area corresponding to each cluster; Indicates The i-th pixel point on the edge of the area corresponding to the cluster, the The absolute value of the slope difference between pixels.

7. The rapid detection method for distribution box components according to claim 1 is characterized in that: The image enhancement is performed according to the enhancement degree of different regions in the grayscale image to obtain the enhanced image, and the specific steps include the following: According to the enhancement degree of different regions in the grayscale image, an adaptive contrast enhancement algorithm is used to enhance each region on the grayscale image to obtain an enhanced image.

8. The rapid detection method for distribution box components according to claim 1 is characterized in that: The defect recognition of the flexible circuit board according to the enhanced image includes the following specific steps: The template matching algorithm is used to obtain abnormal matching points on the enhanced image, and the abnormal matching points are used as defective areas in the flexible circuit board.

9. A rapid detection system for a distribution box assembly, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the rapid detection method for distribution box components according to any one of claims 1 to 8 are implemented.

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