Mining part processing quality detection method, device and system

Abstract

The invention relates to the technical field of image detection, in particular to a mining part machining quality detection method, device and system, and the method comprises the steps: obtaining a surface grayscale image of a mining part in each direction; determining the direction dispersion of each edge contour in the surface grayscale image of each orientation; determining the gray dispersion of each edge contour in the surface gray image of each orientation; determining a color feature value of each edge contour in the surface grayscale image of each orientation; determining the drift trend degree of each edge contour in the surface grayscale image of each orientation; determining a shape feature value of each edge contour in the surface grayscale image of each orientation; and determining a discrimination coefficient of each edge contour in the surface gray level image in each direction, and detecting the processing quality of the mining part. According to the method, the accuracy of detecting the surface defects of the mining parts is improved, and normal operation of mining equipment and safe production of mining operation are facilitated.

Description

Technical Field

[0001] The present application relates to the field of image detection technology, and in particular to a method, device and system for detecting the processing quality of mining parts. Background Art

[0002] Mining parts play a vital role in mining operations. The processing quality of these parts not only directly affects the operating reliability and production efficiency of the equipment, but also the safety and stability of mining operations. Therefore, strict processing quality inspection of mining parts is of great significance to ensure the normal operation of equipment and safe production in mines.

[0003] When inspecting the processing quality of mining parts, traditional inspection methods implement defect detection by threshold segmentation based on the grayscale value of the image. When the grayscale value of the defective area is close to that of the normal area, for example, when the grayscale of the pixel points at the crack area and the corners on the surface of the mining parts are close, missed detection may occur, resulting in a low accuracy rate for defect detection of mining parts, affecting the accuracy of quality inspection of mining parts. Summary of the invention

[0004] In order to solve the above technical problems, a method, equipment and system for detecting the processing quality of mining parts are provided to solve the existing problems.

[0005] The solution to the technical problem of this application is to provide a method, device and system for detecting the processing quality of mining parts, including the following steps:

[0006] In a first aspect, an embodiment of the present application provides a method for detecting the processing quality of mining parts, the method comprising the following steps:

[0007] Obtain the surface grayscale image of each orientation of the mining parts, and extract each edge contour in the surface grayscale image of each orientation;

[0008] Determine the directional discreteness of each edge contour in the surface grayscale image of each orientation according to the discreteness of the gradient direction of the edge pixel points on each edge contour; determine the grayscale discreteness of each edge contour in the surface grayscale image of each orientation according to the discreteness of the distribution of the grayscale values ​​of the pixel points in the area surrounded by each edge contour; determine the color feature value of each edge contour in the surface grayscale image of each orientation according to the fluctuation range of the grayscale values ​​of the pixel points in the area surrounded by each edge contour, combined with the directional discreteness and the grayscale discreteness;

[0009] According to the curvature characteristics of the edge pixels on each edge contour and the change in the drift direction of the edge pixels corresponding to the extreme values ​​of all curvatures after mean shift, the drift trend of each edge contour in the surface grayscale image of each orientation is determined; according to the proportion of the number of edge pixels corresponding to the extreme values ​​of all curvatures on each edge contour, combined with the drift trend, the shape characteristic value of each edge contour in the surface grayscale image of each orientation is determined;

[0010] Based on the color feature values ​​and the shape feature values, the discrimination coefficient of each edge contour in the surface grayscale image of each orientation is determined, and based on the discrimination coefficients of all orientations, the processing quality of mining parts is detected.

[0011] Preferably, the directional discreteness of each edge contour in the surface grayscale image of each orientation is the variance of the gradient direction angles of all edge pixel points on each edge contour.

[0012] Preferably, the step of determining the grayscale discreteness of each edge contour in the surface grayscale image at each orientation includes:

[0013] The pixels within the area surrounded by each edge contour are recorded as internal pixels;

[0014] Calculate the average grayscale value of any internal pixel point of each edge contour and all pixels in its neighborhood, and record it as the local grayscale of any internal pixel point;

[0015] The discrete degree of the local grayscale of all internal pixel points of each edge contour is used as the grayscale discrete degree of each edge contour in the surface grayscale image of each orientation.

[0016] Preferably, determining the color feature value of each edge contour in the surface grayscale image at each orientation includes:

[0017] Calculate the local grayscale range of all internal pixel points of each edge contour;

[0018] The product of the range, the grayscale dispersion and the directional dispersion is used as the color feature value of each edge contour in the surface grayscale image of each orientation.

[0019] Preferably, the step of determining the drift tendency of each edge contour in the surface grayscale image at each orientation includes:

[0020] Taking any edge pixel point on each edge contour as the starting point, all edge pixel points on each edge contour are arranged in a clockwise direction to obtain the extreme value of the curvature of all edge pixel points on each edge contour;

[0021] All the extreme value corresponding edge pixels are recorded as extreme value pixels; the position coordinates of all extreme value pixels on each edge contour are used as the input of the mean shift algorithm to obtain the drift direction angle of each extreme value pixel in each iteration;

[0022] The average of the drift direction angles of all extreme pixel points on each edge contour in the surface grayscale image of each orientation in all iterations is taken as the drift tendency degree of each edge contour in the surface grayscale image of each orientation.

[0023] Preferably, determining the shape feature value of each edge contour in the surface grayscale image at each orientation includes:

[0024] Count the number of all extreme pixel points on each edge contour, and record it as the first number;

[0025] Count the number of all edge pixels on each edge contour, and record it as the second number;

[0026] The ratio of the first quantity to the second quantity is calculated, and the product of the inverse of the drift tendency degree and the ratio is used as the shape feature value of each edge contour in the surface grayscale image of each orientation.

[0027] Preferably, the discrimination coefficient of each edge contour in the surface grayscale image of each orientation is a normalized result of the product of the color feature value and the shape feature value.

[0028] Preferably, the processing quality inspection of mining parts includes: if there is an edge contour with the discrimination coefficient greater than a preset threshold in the surface grayscale image of all orientations, the mining parts are defective; otherwise, the mining parts are not defective.

[0029] In the second aspect, an embodiment of the present application also provides a mining parts processing quality inspection device, including a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, the steps of any one of the above-mentioned mining parts processing quality inspection methods are implemented.

[0030] In a third aspect, an embodiment of the present application further provides a system for detecting the processing quality of mining parts, wherein a computer program is stored in the system, and when the computer program is executed by a processor, the steps of any one of the above-mentioned methods for detecting the processing quality of mining parts are implemented.

[0031] This application has at least the following beneficial effects:

[0032] The present application determines the directional discreteness of each edge contour in the surface grayscale image of each orientation according to the discreteness of the gradient direction of the edge pixel points on each edge contour, and its beneficial effect is that the gradient direction change of the edge pixel points is taken into account to reflect the possibility that the corresponding edge contour is a crack; the grayscale discreteness of each edge contour in the surface grayscale image of each orientation is determined according to the distribution discreteness of the grayscale values ​​of the pixels in the area surrounded by each edge contour, and its beneficial effect is that the grayscale change of the pixels inside the edge contour caused by the depth of the crack is taken into account to reflect the distribution of the grayscale values ​​of the pixels, and further indicate the possibility that the contour edge is a crack; according to the fluctuation range of the grayscale values ​​of the pixels in the area surrounded by each edge contour, combined with the directional discreteness and the grayscale discreteness, the color feature value of each edge contour in the surface grayscale image of each orientation is determined, and its beneficial effect is that the characteristic difference between the crack and the angular contour is further explained by combining the change of the gradient direction of the edge pixel points and the grayscale change of the pixels inside the edge contour, so as to reflect the possibility that the edge contour is a crack; according to the curvature characteristics of the edge pixel points on each edge contour, and the extremes of all curvatures The change of drift direction of edge pixels corresponding to the value after mean shift is performed, and the drift trend of each edge contour in the surface grayscale image of each orientation is determined. The beneficial effect is that the curvature change of edge pixels is taken into account, and the change of drift direction of edge pixels corresponding to the extreme value of curvature in each drift process is analyzed to reflect the possibility that the corresponding edge pixel is located in the crack; according to the proportion of the number of edge pixels corresponding to the extreme values ​​of all curvatures on each edge contour, combined with the drift trend, the shape feature value of each edge contour in the surface grayscale image of each orientation is determined. Its beneficial effect is that it takes into account the difference in curvature changes of edge pixels on cracks and corners, as well as the consistency of drift directions, to further reflect the possibility that the corresponding edge pixels are located in the cracks; based on the color eigenvalues ​​and the shape eigenvalues, the discrimination coefficient of each edge contour in the surface grayscale image of each orientation is determined to detect the processing quality of mining parts. Its beneficial effect is that it can more accurately detect crack defects in mining parts, improves the accuracy of surface defect detection of mining parts, and is beneficial to the normal operation of mining equipment and safe production of mining operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] A method for detecting the processing quality of mining parts of the present application is further described in detail below in conjunction with the accompanying drawings.

[0034] Figure 1 A flowchart of a method for detecting the processing quality of mining parts provided in an embodiment of the present application;

[0035] Figure 2A flowchart of the steps of a method for obtaining the color feature value of each edge contour in a surface grayscale image of each orientation provided in an embodiment of the present application;

[0036] Figure 3 A flowchart of the steps of a method for obtaining shape feature values ​​of each edge contour in a surface grayscale image at each orientation provided in an embodiment of the present application. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution and advantages of this application more clear, the following is a further detailed description of a method, device and system for detecting the processing quality of mining parts proposed in this application in combination with the accompanying drawings and implementation examples. It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit this application.

[0038] 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.

[0039] See also Figure 1 , which shows a flowchart of a method for detecting the processing quality of mining parts provided by an embodiment of the present application, the method comprising the following steps:

[0040] Step 1: Obtain the surface grayscale image of each orientation of the mining parts.

[0041] A group of CCD cameras are placed around a mining component to collect surface images of the mining component. The collected images are denoised, enhanced, sharpened and grayed to obtain surface gray images in each orientation.

[0042] Preferably, in this embodiment, a median filtering algorithm is used to denoise the image, a histogram equalization algorithm is used to enhance the image, and a Laplace algorithm is used to sharpen the image, wherein the median filtering algorithm, the histogram equalization algorithm, and the Laplace algorithm are well-known technologies and will not be described in detail here.

[0043] At this point, the surface grayscale image at each orientation is obtained.

[0044] Step 2: determine the directional discreteness of each edge contour in the surface grayscale image of each orientation according to the discreteness of the gradient direction of the edge pixels on each edge contour; determine the grayscale discreteness of each edge contour in the surface grayscale image of each orientation according to the discreteness of the distribution of the grayscale values ​​of the pixels in the area surrounded by each edge contour; determine the color feature value of each edge contour in the surface grayscale image of each orientation according to the fluctuation range of the grayscale values ​​of the pixels in the area surrounded by each edge contour, combined with the directional discreteness and the grayscale discreteness.

[0045] During the casting process of mining parts, due to uneven heating of the casting, it is easy to cause defects in the shape of cracks in the mining parts during the production process, making the mining parts prone to breakage during use. During the production process, it is necessary to detect the mining parts with unqualified processing quality.

[0046] Furthermore, the flowchart of the steps of the method for obtaining the color feature value of each edge contour in the surface grayscale image of each orientation provided by the embodiment of the present application is as follows: Figure 2 shown.

[0047] First, by extracting the edge contours in the surface grayscale image, all the contours that may be cracks in the surface grayscale image are initially extracted, specifically:

[0048] Perform edge detection on the surface grayscale image at each orientation, extract each edge contour, and the gradient direction angle of each edge pixel point;

[0049] Preferably, in this embodiment, a canny edge detection algorithm is used for edge detection, wherein the canny edge detection algorithm is a well-known technology and will not be described in detail here.

[0050] Furthermore, all the extracted edge contours include angular contours in the normal structure of the parts and possible crack contours. In order to detect whether there is a crack contour from the edge contour, the characteristics of all edge contours are analyzed to distinguish between angular contours and crack contours.

[0051] Since the crack may be irregular, the color change of the pixels on both sides of the crack outline may be from light to dark or from dark to light, and this change is irregular; while on both sides of the angular outline, the color change is either from light to dark or from dark to light, and the color change on both sides is consistent, so the change in the gradient direction of the edge pixels on the crack outline will be more drastic than the change in the gradient direction of the edge pixels on the angular outline; secondly, in the area surrounded by the crack outline, the deeper area has a darker color and the lighter area has a lighter color, and the color distribution of the pixels within the crack outline is uneven, so the grayscale value of the pixels inside the crack outline will show a certain fluctuation.

[0052] Based on the above analysis, the gradient changes of edge pixels on each edge contour and the grayscale changes of pixels in the area surrounded by each edge contour are analyzed to determine the color feature value, which preliminarily reflects the possibility that the edge contour is a crack contour, specifically:

[0053] The variance of the gradient direction angles of all edge pixels on each edge contour in the surface grayscale image of each orientation is used as the directional discreteness of each edge contour in the surface grayscale image of each orientation;

[0054] The pixels within the area surrounded by each edge contour are recorded as internal pixels;

[0055] Calculate the average grayscale value of any internal pixel point of each edge contour and all pixels in its neighborhood, and record it as the local grayscale of any internal pixel point;

[0056] Preferably, in this embodiment, the average grayscale value of any internal pixel point of each edge contour and all pixel points in its 8-neighborhood is calculated and recorded as the local grayscale of any internal pixel point.

[0057] The discrete degree of the local grayscale of all internal pixel points of each edge contour is used as the grayscale discrete degree of each edge contour;

[0058] Preferably, in this embodiment, the variance of the local grayscale of all internal pixel points of each edge contour is used as the grayscale dispersion of each edge contour.

[0059] Calculate the local grayscale range of all internal pixel points of each edge contour, and use the product of the range, the grayscale discreteness and the directional discreteness as the color feature value of each edge contour in the surface grayscale image of each orientation;

[0060] Preferably, in this embodiment, the method for calculating the color feature value of each edge contour in the surface grayscale image of each orientation is: m,k =α m,k ×V m,k ×F m,k , where C m,k is the color feature value of the kth edge contour in the surface grayscale image at the mth orientation, α m,k V is the local grayscale range of all internal pixels of the kth edge contour in the surface grayscale image at the mth orientation, m,k is the variance of the local grayscale of all internal pixels of the kth edge contour in the surface grayscale image of the mth orientation, that is, the grayscale dispersion, F m,k It is the variance of the gradient direction angles of all edge pixels on the kth edge contour in the surface grayscale image at the mth orientation, that is, the directional discreteness.

[0061] It should be noted that the larger the directional discreteness is, the more dramatic the change in the gradient direction of the edge pixel points on the corresponding edge contour is, and the more likely the corresponding edge contour is to be a crack contour; secondly, the larger the range and the grayscale discreteness are, the greater the difference in the grayscale values ​​of the internal pixel points of the corresponding edge contour is, and the larger the fluctuation amplitude is, the more likely the corresponding edge contour is to be a crack contour. Therefore, the larger the obtained color feature value is, the greater the possibility that the edge contour is a crack.

[0062] At this point, the color feature value of each edge contour in the surface grayscale image at each orientation is obtained.

[0063] Step 3: Determine the drift trend of each edge contour in the surface grayscale image at each orientation based on the curvature characteristics of the edge pixels on each edge contour and the change in drift direction after mean shift of the edge pixels corresponding to the extreme values ​​of all curvatures; determine the shape characteristic value of each edge contour in the surface grayscale image at each orientation based on the proportion of the number of edge pixels corresponding to the extreme values ​​of all curvatures on each edge contour and the drift trend.

[0064] Furthermore, the shape of the crack contour is more tortuous, while the shape of the angular contour is mostly smoother. Among the curvatures of all edge pixels on each edge contour, the larger the proportion of extreme values, the more likely it is a crack; secondly, the distribution density of edge pixels corresponding to different extreme values ​​is different. When the extreme values ​​are mean-shifted, the edge pixels corresponding to different extreme values ​​will move toward the most complex area of ​​the crack, and the drift direction of the edge pixels on the crack contour will not change much during each drift process; and for all edge pixels on the angular contour, even if there are extreme values ​​of the curvature of multiple edge pixels due to wear or the complexity of the mechanical structure, the complexity of the edge pixels corresponding to each extreme value is usually similar, that is, the local density of the edge pixels corresponding to each extreme value is close. When the mean shift is performed, the moving direction of the edge pixels corresponding to each extreme value is not fixed, and the drift direction of the edge pixels on the angular contour will change greatly during each drift process.

[0065] Furthermore, the flowchart of the steps of the method for obtaining the shape feature value of each edge contour in the surface grayscale image of each orientation provided by the embodiment of the present application is as follows: Figure 3 shown.

[0066] Based on the above analysis, the curvature change of the edge pixel points on each edge contour is analyzed to determine the shape feature value to reflect the possibility that the corresponding edge contour is a crack contour, specifically:

[0067] Taking any edge pixel point on each edge contour in the surface grayscale image of each orientation as the starting point, all edge pixel points on each edge contour are arranged in a clockwise direction to obtain the extreme value of the curvature of all edge pixel points on each edge contour;

[0068] Preferably, in this embodiment, a differential method is used to obtain extreme values, wherein the differential method is a well-known technology and will not be described in detail here.

[0069] All extreme value corresponding edge pixels are recorded as extreme value pixels, and the position coordinates of all extreme value pixels on each edge contour are used as the input of the mean shift algorithm to obtain the drift direction angle of each extreme value pixel in each iteration;

[0070] Preferably, in this embodiment, the mean shift algorithm is a well-known technology and will not be described in detail here; the bandwidth of the mean shift algorithm is set to 6. As other implementation methods, the implementer can set it according to the actual situation.

[0071] It should be noted that, for ease of understanding, assuming that the original position coordinates of a certain extreme pixel point are (x0, y0), after one iteration of the mean shift algorithm, the new position coordinates are (x1, y1), then the drift direction angle of this iteration is

[0072]

[0073] The average of all iterations of drift direction angles of all extreme pixel points on each edge contour in the surface grayscale image of each orientation is taken as the drift tendency degree of each edge contour in the surface grayscale image of each orientation;

[0074] It should be noted that the larger the drift trend is, the greater the difference in the drift direction of the extreme pixel point after each iteration, and the corresponding extreme pixel point is more likely to be on the angular contour. Conversely, the smaller the drift trend is, the more consistent the drift direction of the extreme pixel point after each iteration, and the corresponding extreme pixel point is more likely to be on the crack contour.

[0075] Count the number of all extreme pixel points on each edge contour, and record it as the first number;

[0076] Count the number of all edge pixels on each edge contour, and record it as the second number;

[0077] Calculating a ratio of the first quantity to the second quantity, and taking the product of the inverse of the drift tendency degree and the ratio as a shape feature value of each edge contour in the surface grayscale image of each orientation;

[0078] Preferably, in this embodiment, the shape feature value of each edge contour in the surface grayscale image of each orientation is calculated as follows: Among them, Z m,k is the shape feature value of the kth edge contour in the surface grayscale image at the mth orientation, n m,k is the number of all extreme pixel points on the kth edge contour in the surface grayscale image at the mth orientation, that is, the first number, N m,k is the number of all edge pixels on the kth edge contour in the surface grayscale image at the mth orientation, that is, the second number, is the drift tendency of the kth edge profile in the surface grayscale image at the mth orientation.

[0079] It should be noted that the larger the ratio is, the greater the number of extreme pixel points in the corresponding edge contour, which means that the corresponding edge contour is more likely to be a crack contour; secondly, the smaller the drift trend is, the more consistent the drift direction of the extreme pixel points after each iteration, which means that the corresponding extreme pixel points are more likely to be on the crack contour, and the larger the obtained shape eigenvalue is, the more likely the edge contour is to be a crack.

[0080] At this point, the shape feature value of each edge contour in the surface grayscale image at each orientation is obtained.

[0081] Step 4: Determine the discrimination coefficient of each edge contour in the surface grayscale image at each orientation based on the color feature value and the shape feature value, and detect the processing quality of mining parts based on the discrimination coefficients at all orientations.

[0082] Further, based on the color feature value and the shape feature value, a discrimination coefficient is determined, specifically:

[0083] The normalized result of the product of the color feature value and the shape feature value is used as the discrimination coefficient of each edge contour in the surface grayscale image of each orientation;

[0084] Preferably, in this embodiment, an inverse tangent function is used for normalization processing, wherein the inverse tangent function is a well-known technology and will not be described in detail here; in this embodiment, the calculation method of the crack discrimination coefficient of each edge contour in the surface grayscale image of each orientation is: Among them, w m,k is the discriminant coefficient of the kth edge contour in the surface grayscale image at the mth orientation, C m,k is the color feature value of the kth edge contour in the surface grayscale image at the mth orientation, Z m,k is the shape eigenvalue of the kth edge contour in the surface grayscale image at the mth orientation, and arctan() is the inverse tangent function.

[0085] It should be noted that the larger the discrimination coefficient is, the more likely the corresponding edge contour is a crack contour.

[0086] Further, based on the discrimination coefficient, the processing quality of mining parts is tested, specifically:

[0087] If there is an edge contour with the discrimination coefficient greater than a preset threshold value in the surface grayscale images of all directions, the mining component has a defect; otherwise, the mining component does not have a defect.

[0088] Preferably, in this embodiment, the preset threshold value is 0.85. As other implementation modes, the implementer can set it according to the actual situation.

[0089] An embodiment of the present application also provides a mining parts processing quality inspection device, including a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, the steps of any one of the above-mentioned mining parts processing quality inspection methods are implemented.

[0090] Based on the same inventive concept as the above method, an embodiment of the present application also provides a mining parts processing quality detection system, in which a computer program is stored, and when the computer program is executed by a processor, the steps of any one of the above-mentioned mining parts processing quality detection methods are implemented.

[0091] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0092] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0093] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the present application. It should be pointed out that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present application without departing from the content of the technical solution of the present application, shall fall within the protection scope of the technical solution of the present application.

Claims

1. A method for detecting the processing quality of mining parts, characterized in that: The method comprises the following steps: Obtain the surface grayscale image of each orientation of the mining parts, and extract each edge contour in the surface grayscale image of each orientation; Determine the directional discreteness of each edge contour in the surface grayscale image of each orientation according to the discreteness of the gradient direction of the edge pixel points on each edge contour; determine the grayscale discreteness of each edge contour in the surface grayscale image of each orientation according to the discreteness of the distribution of the grayscale values ​​of the pixel points in the area surrounded by each edge contour; determine the color feature value of each edge contour in the surface grayscale image of each orientation according to the fluctuation range of the grayscale values ​​of the pixel points in the area surrounded by each edge contour, combined with the directional discreteness and the grayscale discreteness; According to the curvature characteristics of the edge pixels on each edge contour and the change in the drift direction of the edge pixels corresponding to the extreme values ​​of all curvatures after mean shift, the drift trend of each edge contour in the surface grayscale image of each orientation is determined; according to the proportion of the number of edge pixels corresponding to the extreme values ​​of all curvatures on each edge contour, combined with the drift trend, the shape characteristic value of each edge contour in the surface grayscale image of each orientation is determined; Based on the color feature values ​​and the shape feature values, the discrimination coefficient of each edge contour in the surface grayscale image of each orientation is determined, and based on the discrimination coefficients of all orientations, the processing quality of mining parts is detected.

2. A method for detecting the processing quality of mining parts as claimed in claim 1, characterized in that: The directional dispersion of each edge contour in the surface grayscale image of each orientation is the variance of the gradient direction angles of all edge pixel points on each edge contour.

3. A method for detecting the processing quality of mining parts as claimed in claim 1, characterized in that: Determining the grayscale discreteness of each edge contour in the surface grayscale image at each orientation includes: The pixels within the area surrounded by each edge contour are recorded as internal pixels; Calculate the average grayscale value of any internal pixel point of each edge contour and all pixels in its neighborhood, and record it as the local grayscale of any internal pixel point; The discrete degree of the local grayscale of all internal pixel points of each edge contour is used as the grayscale discrete degree of each edge contour in the surface grayscale image of each orientation.

4. A method for detecting the processing quality of mining parts as claimed in claim 3, characterized in that: The step of determining the color feature value of each edge contour in the surface grayscale image at each orientation includes: Calculate the local grayscale range of all internal pixel points of each edge contour; The product of the range, the grayscale dispersion and the directional dispersion is used as the color feature value of each edge contour in the surface grayscale image of each orientation.

5. A method for detecting the processing quality of mining parts as claimed in claim 1, characterized in that: Determining the drift tendency of each edge contour in the surface grayscale image at each orientation includes: Taking any edge pixel point on each edge contour as the starting point, all edge pixel points on each edge contour are arranged in a clockwise direction to obtain the extreme value of the curvature of all edge pixel points on each edge contour; All the extreme value corresponding edge pixels are recorded as extreme value pixels; the position coordinates of all extreme value pixels on each edge contour are used as the input of the mean shift algorithm to obtain the drift direction angle of each extreme value pixel in each iteration; The average of the drift direction angles of all extreme pixel points on each edge contour in the surface grayscale image of each orientation in all iterations is taken as the drift tendency degree of each edge contour in the surface grayscale image of each orientation.

6. A method for detecting the processing quality of mining parts as claimed in claim 5, characterized in that: The step of determining the shape feature value of each edge contour in the surface grayscale image at each orientation includes: Count the number of all extreme pixel points on each edge contour, and record it as the first number; Count the number of all edge pixels on each edge contour, and record it as the second number; The ratio of the first quantity to the second quantity is calculated, and the product of the inverse of the drift tendency degree and the ratio is used as the shape feature value of each edge contour in the surface grayscale image of each orientation.

7. A method for detecting the processing quality of mining parts as claimed in claim 1, characterized in that: The discrimination coefficient of each edge contour in the surface grayscale image of each orientation is a normalized result of the product of the color feature value and the shape feature value.

8. A method for detecting the processing quality of mining parts as claimed in claim 1, characterized in that: The detection of the processing quality of mining parts includes: if there is an edge contour with a discrimination coefficient greater than a preset threshold in the surface grayscale image of all directions, the mining parts have defects, otherwise, the mining parts do not have defects.

9. A mining parts processing quality inspection equipment, characterized in that: The device includes a memory, a processor, and a computer program stored in the memory and running on the processor, and is characterized in that when the processor executes the computer program, the steps of a mining parts processing quality detection method as described in any one of claims 1-8 are implemented.

10. A mining parts processing quality inspection system, wherein a computer program is stored in the system, characterized in that: When the computer program is executed by a processor, the steps of a method for detecting the processing quality of mining parts as described in any one of claims 1 to 8 are implemented.

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