A friction plate quality detection system based on machine vision

Abstract

The present invention relates to the technical field of friction plates, and in particular to a friction plate quality detection system based on machine vision, comprising: an acquisition module, used for acquiring image data of a friction plate use surface and a friction plate use surface flatness parameter; an evaluation module, used for receiving the friction plate use surface flatness parameter acquired in the acquisition module, and evaluating a friction plate quality detection correction factor based on the friction plate use surface flatness parameter; an analysis module, used for receiving the friction plate use surface image data acquired in the acquisition module, and analyzing the friction plate quality based on the friction plate use surface image data; the present invention realizes multi-party detection of the friction plate surface by using a high-definition camera equipped with a macro lens and in combination with a distance measuring device, so that the friction plate can obtain a high-precision and global quality detection effect, thereby maintaining the factory quality of the friction plate and ensuring that the factory friction plate can serve its loading equipment with more excellent performance.

Description

Technical Field

[0001] The invention relates to the technical field of friction plates, and in particular to a friction plate quality detection system based on machine vision. Background Art

[0002] A friction plate is a component consisting of a core plate and a friction lining or friction material layer, and is widely used in mechanical engineering, machine parts and clutches.

[0003] The invention patent with application number 202110511142.4 discloses a method for detecting defects in a micromotor friction plate based on machine vision, which is characterized in that it includes the following steps: placing the part to be inspected on a stage (11), and collecting an image of the friction plate to be inspected by a camera (15); the camera (15) transmits the collected image to an industrial computer (3); the industrial computer (3) binarizes the collected image of the friction plate to be inspected to obtain a binary image: the target is displayed in white and the background is displayed in black, which is used to extract the target features; the binary image is closed by a circular kernel, and the gap on the boundary of the friction plate is filled without changing the size and position of the boundary of the friction plate to be inspected; the image after the closing operation is The image is filled with water, and the white area outside the boundary of the friction plate to be inspected is filled with black, so as to remove the uninterested area outside the boundary of the friction plate to be inspected: a point in the white area outside the boundary of the friction plate to be inspected is selected as the starting point, and the white area where the point is located is filled with black, so that the filled area becomes the background, so as to reduce the interference of the uninterested area on the extraction of the friction plate contour; the contour of the friction plate to be inspected after the flood filling is extracted: the "RETREXTERNAL" method of the findContours function is used to detect the outermost contour of the friction plate to be inspected, so as to avoid the interference of the inner area of ​​the boundary of the friction plate to be inspected: the "CHAIN ​​APPROX NONE" method of the findContours function is used to obtain each pixel position of the outermost contour of the friction plate to be inspected.

[0004] The application aims to solve the problem that "due to the small size of the friction plate in the micromotor, which is only about 8mm, manual inspection is prone to visual fatigue and leads to false detection, and is inefficient, which is not conducive to automated production needs."

[0005] However, most of the existing friction plate quality inspection technologies use simple machine vision to inspect the appearance of the friction plate, and their inspection accuracy is low.

[0006] To this end, we proposed a friction plate quality inspection system based on machine vision. Summary of the invention

[0007] In view of the above-mentioned shortcomings of the prior art, the present invention provides a friction plate quality detection system based on machine vision, which solves the technical problems raised in the above-mentioned background technology.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] A friction plate quality detection system based on machine vision, comprising:

[0010] An acquisition module is used to acquire image data of the friction plate use surface and smoothness parameters of the friction plate use surface; an evaluation module is used to receive the smoothness parameters of the friction plate use surface acquired in the acquisition module, and evaluate the friction plate quality detection correction factor based on the friction plate use surface smoothness parameters; an analysis module is used to receive the image data of the friction plate use surface acquired in the acquisition module, and analyze the friction plate quality based on the image data of the friction plate use surface; a correction module is used to receive the friction plate quality detection correction factor evaluated in the evaluation module and the friction plate quality analysis result in the analysis module, and correct the friction plate quality analysis result by the friction plate quality detection correction factor; a determination module is used to set a friction plate quality qualified determination threshold, receive the friction plate quality analysis result after correction processing in the correction module, and determine whether the friction plate quality is qualified based on the comparison between the friction plate quality qualified determination threshold and the friction plate quality analysis result after correction processing; a feedback module is used to receive the determination result of the determination module in real time, and when the determination result of the determination module is no, the currently received determination result is fed back to the system end user.

[0011] Furthermore, the acquisition module is provided with submodules at the lower level, including:

[0012] A camera module, used to collect image data of the friction plate use surface;

[0013] Distance measurement module, used to measure the distance from itself to the use surface of the friction plate;

[0014] Among them, the camera module is integrated by an ultra-clear camera equipped with a macro lens. The camera module operates to collect several groups of friction plate usage surface image data. The sets of local areas on the friction plate usage surface corresponding to the several groups of friction plate usage surface image data contain the complete friction plate usage surface. Several groups of ranging modules are arranged. The several groups of ranging modules are distributed in an array shape, and the ranging ends of the several groups of ranging modules are in the same plane.

[0015] Furthermore, the friction plate use surface is placed on the electric turntable when performing image data acquisition and flatness parameter acquisition. The electric turntable is operated at least three times during the operation phase of the camera module and the distance measurement module. Before each movement of the electric turntable, the camera module and the distance measurement module are operated once.

[0016] The ranging result of the ranging module operation is the flatness parameter of the friction plate surface. The friction plate placement surface on the electric turntable is parallel to the plane defined by several groups of ranging ends. The friction plate surface image data and the friction plate surface flatness parameter collected by the camera module are marked based on the collection timestamp, and after the marking is completed, they are further stored in the collection module.

[0017] Furthermore, the area defined by the distance measurement modules distributed in an array is larger than the use surface of the friction plate, and the evaluation logic of the friction plate quality detection correction factor in the evaluation module is expressed as:

[0018]

[0019] Where: λ is the correction factor obtained based on an image of the friction plate surface; n is the set of ranging results of the ranging module; d i is the value of the i-th group of distance measurement results; λ′ is the friction plate quality detection correction factor; λ 1 , 2 , 3 is the correction factor obtained based on the friction plate surface image data collected in the first, second and third frames; ω 1 ,ω 2 ,ω 3 is the weight; α is the normalization factor; m is the total number of ranging modules distributed in the array;

[0020] in, Table pair The average weight ω 1 ,ω 2 ,ω 3 The values ​​of are all greater than zero and less than one, and the sum of all weights is 1. The normalization factor α limits the value of the friction plate quality detection correction factor to be within the range of (0, 1].

[0021] Furthermore, after the friction plate quality detection correction factor λ′ is output based on the friction plate quality detection correction factor evaluation logic, it is synchronously fed back to the acquisition module, and the friction plate use surface flatness parameter stored in the module is iterated with the friction plate quality detection correction factor λ′;

[0022] The analysis module monitors the friction plate quality in real time to detect whether the iteration operation of the correction factor λ′ in the acquisition module is completed, and triggers the operation after monitoring the completion of the iteration operation to detect the quality of the friction plate use surface.

[0023] Furthermore, the analysis module is provided with submodules at the lower level, including:

[0024] A traversal unit, used for traversing the timestamps marked by the friction plate usage surface image data stored in the acquisition module;

[0025] A retrieving unit is used to obtain the traversal result of the traversal unit, retrieve the friction plate use surface image data stored in the acquisition module based on the time sequence, and synchronously feed back the retrieved friction plate use surface image data to the analysis module;

[0026] Among them, the retrieval unit monitors in real time whether the analysis operation of the friction plate usage surface image data by the analysis module is completed. After the analysis of the friction plate usage surface image data is completed, another set of friction plate usage surface image data is retrieved and fed back to the analysis module again until all the friction plate usage surface image data stored in the acquisition module are retrieved once.

[0027] Furthermore, the friction plate quality analysis logic in the analysis module is expressed as:

[0028]

[0029] Where: f is the friction plate quality performance value; u is the total amount of the sub-friction plate usage surface images obtained by segmenting the friction plate usage surface image; p v is the total amount of closed contour graphics contained in the surface image of the friction plate of group v; γ is the adjustment factor; d min d max is the maximum diameter and minimum diameter of the closed contour figure in the surface image of each sub-friction plate; N is the set of pixels in the surface image of the friction plate; g q is the gray value of the qth pixel; μ is the gray mean value of the friction plate surface image;

[0030] Among them, the adjustment factor γ is 1 or -1, p v >p v+1 , then the adjustment factor γ is -1, p v ≤p v+1 , then the adjustment factor γ is 1, Table pair The larger the friction plate quality performance value f is, the better the friction plate quality is, and vice versa, the worse the friction plate quality is.

[0031] Based on the above formula, the friction plate quality performance value is obtained for each friction plate use surface image, and the sum and average of the obtained results are further calculated to finally obtain the average result F;

[0032] When the friction plate quality performance value f is obtained based on the above formula, the friction plate use surface image is segmented so that each segmented sub-friction plate use surface image has the same size and shape, and when the friction plate use surface image is segmented into sub-friction plate use surface images, the number of segmentations obeys d min With d max The larger the difference, the more divisions there are, and vice versa.

[0033] Furthermore, the correction operation for the friction plate quality analysis result in the correction module is:

[0034] f′=F×λ′ -1 ;

[0035] Where: f′ is the corrected friction plate quality performance value, that is, the corrected friction plate quality analysis result.

[0036] Furthermore, the feedback module is connected to the mobile computer device held by the user through a wireless network, the feedback target of the feedback module is the mobile computer device held by the user, and the system end user reads the determination result received by the feedback module on the mobile computer device.

[0037] Furthermore, the acquisition module is interactively connected to a camera module and a ranging module via a wireless network, the acquisition module is interactively connected to an evaluation module and an analysis module via a wireless network, the analysis module is interactively connected to a traversal unit and a retrieval unit via a wireless network, the traversal unit and the retrieval unit are interactively connected to the acquisition module via a wireless network, and the analysis module is interactively connected to a correction module, a determination module and a feedback module via a wireless network.

[0038] Compared with the known public technology, the technical solution provided by the present invention has the following beneficial effects:

[0039] The present invention provides a friction plate quality inspection system based on machine vision. During operation, the system realizes multi-faceted inspection of the friction plate surface through a high-definition camera equipped with a macro lens and in cooperation with a distance measuring device, so that the friction plate can obtain a high-precision and global quality inspection effect, thereby maintaining the factory quality of the friction plate and ensuring that the factory friction plate can serve its loading equipment with more excellent performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0041] Figure 1 This is a structural schematic diagram of a friction plate quality detection system based on machine vision. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] The present invention will be further described below in conjunction with the embodiments.

[0044] Embodiment 1:

[0045] A friction plate quality detection system based on machine vision in this embodiment, such as Figure 1 As shown, including:

[0046] An acquisition module is used to acquire image data of the friction plate use surface and smoothness parameters of the friction plate use surface;

[0047] The acquisition module is equipped with submodules, including:

[0048] A camera module, used to collect image data of the friction plate use surface;

[0049] Distance measurement module, used to measure the distance from itself to the use surface of the friction plate;

[0050] The camera module is integrated by an ultra-clear camera equipped with a macro lens. The camera module collects several groups of friction plate use surface image data. The sets of local areas on the friction plate use surface corresponding to the several groups of friction plate use surface image data contain the complete friction plate use surface. The distance measurement module is provided with several groups. The several groups of distance measurement modules are distributed in an array shape. The distance measurement ends of the several groups of distance measurement modules are in the same plane.

[0051] An evaluation module, used for receiving the friction plate surface flatness parameter collected in the collection module, and evaluating the friction plate quality detection correction factor based on the friction plate surface flatness parameter;

[0052] The area defined by the distance measurement modules distributed in an array is larger than the use surface of the friction plate. The evaluation logic of the friction plate quality detection correction factor in the evaluation module is expressed as follows:

[0053]

[0054] Where: λ is the correction factor obtained based on an image of the friction plate surface; n is the set of ranging results of the ranging module; d i is the value of the i-th group of distance measurement results; λ′ is the friction plate quality detection correction factor; λ 1 , 2 , 3is the correction factor obtained based on the friction plate surface image data collected in the first, second and third frames; ω 1 ,ω 2 ,ω 3 is the weight; α is the normalization factor; m is the total number of ranging modules distributed in the array;

[0055] in, Table pair The average weight ω 1 ,ω 2 ,ω 3 The values ​​of are all greater than zero and less than one, and the sum of all weights is 1. The normalization factor α limits the value of the friction plate quality detection correction factor to be within the range of (0, 1];

[0056] After the friction plate quality detection correction factor λ′ is output based on the friction plate quality detection correction factor evaluation logic, it is synchronously fed back to the acquisition module, and the friction plate use surface flatness parameter stored in the module is iterated with the friction plate quality detection correction factor λ′;

[0057] The analysis module runs to monitor the quality of the friction plate in real time to detect whether the iteration operation of the correction factor λ′ in the acquisition module is completed. When the iteration operation is detected to be completed, the operation is triggered to detect the quality of the friction plate use surface;

[0058] An analysis module, used for receiving the image data of the friction plate use surface collected by the collection module, and analyzing the quality of the friction plate based on the image data of the friction plate use surface;

[0059] The analysis module is provided with submodules, including:

[0060] A traversal unit, used for traversing the timestamps marked by the friction plate usage surface image data stored in the acquisition module;

[0061] A retrieving unit is used to obtain the traversal result of the traversal unit, retrieve the friction plate use surface image data stored in the acquisition module based on the time sequence, and synchronously feed back the retrieved friction plate use surface image data to the analysis module;

[0062] The retrieval unit monitors in real time whether the analysis operation of the friction plate use surface image data by the analysis module is completed. After the analysis of the friction plate use surface image data is completed, a group of friction plate use surface image data is retrieved again and feedback is given to the analysis module again until all the friction plate use surface image data stored in the acquisition module are retrieved once;

[0063] The friction plate quality analysis logic in the analysis module is expressed as:

[0064]

[0065] Where: f is the friction plate quality performance value; u is the total amount of the sub-friction plate usage surface images obtained by segmenting the friction plate usage surface image; p v is the total amount of closed contour graphics contained in the surface image of the friction plate of group v; γ is the adjustment factor; d min d max is the maximum diameter and minimum diameter of the closed contour figure in the surface image of each sub-friction plate; N is the set of pixels in the surface image of the friction plate; g q is the gray value of the qth pixel; μ is the gray mean value of the friction plate surface image;

[0066] Among them, the adjustment factor γ is 1 or -1, p v >p v+1 , then the adjustment factor γ is -1, p v ≤p v+1 , then the adjustment factor γ is 1, Table pair The larger the friction plate quality performance value f is, the better the friction plate quality is, and vice versa, the worse the friction plate quality is.

[0067] Based on the above formula, the friction plate quality performance value is obtained for each friction plate use surface image, and the sum and average of the obtained results are further calculated to finally obtain the average result F;

[0068] When the friction plate quality performance value f is obtained based on the above formula, the friction plate use surface image is segmented so that each segmented sub-friction plate use surface image has the same size and shape, and when the friction plate use surface image is segmented into sub-friction plate use surface images, the number of segmentations obeys d min With d max The larger the difference, the more divisions there are, and vice versa;

[0069] A correction module is used to receive the friction plate quality detection correction factor evaluated in the evaluation module and the friction plate quality analysis result in the analysis module, and correct the friction plate quality analysis result by using the friction plate quality detection correction factor;

[0070] A determination module is used to set a friction plate quality acceptance determination threshold, receive the friction plate quality analysis result after correction processing in the correction module, and determine whether the friction plate quality is acceptable based on the comparison between the friction plate quality acceptance determination threshold and the friction plate quality analysis result after correction processing;

[0071] A feedback module is used to receive the determination result of the determination module in real time, and when the determination result of the determination module is negative, feed back the currently received determination result to the system end user;

[0072] The acquisition module is interactively connected to the camera module and the ranging module through a wireless network, the acquisition module is interactively connected to the evaluation module and the analysis module through a wireless network, the analysis module is interactively connected to the traversal unit and the retrieval unit through a wireless network, the traversal unit and the retrieval unit are interactively connected to the acquisition module through a wireless network, and the analysis module is interactively connected to the correction module, the judgment module and the feedback module through a wireless network.

[0073] In this embodiment, the acquisition module operates to acquire image data of the friction plate use surface and smoothness parameters of the friction plate use surface, the camera module synchronously acquires image data of the friction plate use surface, the ranging module measures the distance from itself to the friction plate use surface in real time, the evaluation module further receives the smoothness parameters of the friction plate use surface acquired in the acquisition module, and evaluates the friction plate quality detection correction factor based on the smoothness parameters of the friction plate use surface, the analysis module post-operates to receive the image data of the friction plate use surface acquired in the acquisition module, and analyzes the friction plate quality based on the image data of the friction plate use surface, the traversal unit synchronously traverses the timestamps marked by the image data of the friction plate use surface stored in the acquisition module, the retrieval unit obtains the traversal results of the traversal unit in real time, and the friction plate quality detection correction factor stored in the acquisition module is evaluated based on the time sequence. The friction plate usage surface image data is retrieved, and the retrieved friction plate usage surface image data is synchronously fed back to the analysis module; the correction module then receives the friction plate quality detection correction factor evaluated in the evaluation module and the friction plate quality analysis result in the analysis module, and corrects the friction plate quality analysis result by the friction plate quality detection correction factor; the judgment module post-operates to set a qualified judgment threshold for the friction plate quality, receives the corrected friction plate quality analysis result in the correction module, and determines whether the friction plate quality is qualified based on the comparison between the qualified judgment threshold for the friction plate quality and the corrected friction plate quality analysis result; finally, the judgment result of the judgment module is received in real time through the feedback module; when the judgment result of the judgment module is no, the currently received judgment result is fed back to the system end user.

[0074] Through the operation of the system in the above embodiment, a more comprehensive and reliable quality inspection service is provided for the friction plate, ensuring that the friction plate is inspected to obtain a qualified judgment basis, thereby ensuring that the quality of the produced friction plate is guaranteed.

[0075] Embodiment 2:

[0076] In terms of specific implementation, based on Example 1, this example refers to Figure 1 The friction plate quality detection system based on machine vision in Example 1 is further described in detail:

[0077] The friction plate use surface is placed on the electric turntable when performing image data acquisition and flatness parameter acquisition. The electric turntable runs at least three times during the camera module and distance measurement module operation phase. Before each movement of the electric turntable, the camera module and distance measurement module run once.

[0078] The ranging result of the ranging module is the flatness parameter of the friction plate surface. The friction plate placement surface on the electric turntable is parallel to the plane defined by several groups of ranging ends. The friction plate surface image data and the friction plate surface flatness parameter collected by the camera module are marked based on the acquisition timestamp and are further stored in the acquisition module after the marking is completed.

[0079] like Figure 1 As shown, the correction operation for the friction plate quality analysis result in the correction module is:

[0080] f′=F×λ′ -1 ;

[0081] Where: f′ is the corrected friction plate quality performance value, that is, the corrected friction plate quality analysis result.

[0082] like Figure 1 As shown, the feedback module is connected to the mobile computer device held by the user through a wireless network, the feedback target of the feedback module is the mobile computer device held by the user, and the system end user reads the determination result received by the feedback module on the mobile computer device.

[0083] In this embodiment, through the above settings, further operation logic support is provided for the system in Embodiment 1, and the correction operation of the friction plate quality analysis result is further limited.

[0084] In summary, during the operation of the system in the above embodiment, a high-definition camera equipped with a macro lens and a distance measuring device are used to realize multi-faceted detection of the surface of the friction plate, so that the friction plate can obtain a high-precision and global quality detection effect, thereby maintaining the factory quality of the friction plate and ensuring that the friction plate leaving the factory can serve its loading equipment with better performance.

[0085] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A friction plate quality detection system based on machine vision, characterized in that: include: An acquisition module is used to acquire image data of the friction plate use surface and smoothness parameters of the friction plate use surface; An evaluation module, used for receiving the friction plate surface flatness parameter collected in the collection module, and evaluating the friction plate quality detection correction factor based on the friction plate surface flatness parameter; An analysis module, used for receiving the image data of the friction plate use surface collected by the collection module, and analyzing the quality of the friction plate based on the image data of the friction plate use surface; A correction module is used to receive the friction plate quality detection correction factor evaluated in the evaluation module and the friction plate quality analysis result in the analysis module, and correct the friction plate quality analysis result by using the friction plate quality detection correction factor; A determination module is used to set a friction plate quality acceptance determination threshold, receive the friction plate quality analysis result after correction processing in the correction module, and determine whether the friction plate quality is acceptable based on the comparison between the friction plate quality acceptance determination threshold and the friction plate quality analysis result after correction processing; The feedback module is used to receive the determination result of the determination module in real time, and when the determination result of the determination module is negative, feed back the currently received determination result to the system end user; The area defined by the distance measurement modules distributed in an array is larger than the use surface of the friction plate. The evaluation logic of the friction plate quality detection correction factor in the evaluation module is expressed as follows: Where n is the set of ranging results of the ranging module; d i is the value of the i-th group of ranging results; λ′ is the friction plate quality detection correction factor; ω1, ω2, ω3 are weights; α is the normalization factor; m is the total number of ranging modules distributed in the array; in, Table pair The average value of weights ω1, ω2, and ω3 are all greater than zero and less than one, and the sum of all weights is 1. The normalization factor α limits the value of the friction plate quality detection correction factor to be within the range of (0, 1]; After the friction plate quality detection correction factor λ′ is output based on the friction plate quality detection correction factor evaluation logic, it is synchronously fed back to the acquisition module, and the friction plate use surface flatness parameter stored in the module is iterated with the friction plate quality detection correction factor λ′; The analysis module monitors the friction plate quality in real time to detect whether the iteration operation of the correction factor λ′ in the acquisition module is completed, and triggers the operation after monitoring the completion of the iteration operation to detect the quality of the friction plate use surface.

2. The friction plate quality detection system based on machine vision according to claim 1 is characterized in that: The acquisition module is provided with submodules at the lower level, including: A camera module, used to collect image data of the friction plate use surface; Distance measurement module, used to measure the distance from itself to the use surface of the friction plate; Among them, the camera module is integrated by an ultra-clear camera equipped with a macro lens. The camera module operates to collect several groups of friction plate usage surface image data. The sets of local areas on the friction plate usage surface corresponding to the several groups of friction plate usage surface image data contain the complete friction plate usage surface. Several groups of ranging modules are arranged. The several groups of ranging modules are distributed in an array shape, and the ranging ends of the several groups of ranging modules are in the same plane.

3. The friction plate quality detection system based on machine vision according to claim 2 is characterized in that: The friction plate use surface is placed on the electric turntable when performing image data acquisition and flatness parameter acquisition. The electric turntable is operated at least three times during the operation phase of the camera module and the distance measurement module. Before each movement of the electric turntable, the camera module and the distance measurement module are operated once. The ranging result of the ranging module operation is the flatness parameter of the friction plate surface. The friction plate placement surface on the electric turntable is parallel to the plane defined by several groups of ranging ends. The friction plate surface image data and the friction plate surface flatness parameter collected by the camera module are marked based on the collection timestamp, and after the marking is completed, they are further stored in the collection module.

4. The friction plate quality detection system based on machine vision according to claim 1 is characterized in that: The analysis module is provided with submodules at the lower level, including: A traversal unit, used for traversing the timestamps marked by the friction plate usage surface image data stored in the acquisition module; A retrieving unit is used to obtain the traversal result of the traversal unit, retrieve the friction plate use surface image data stored in the acquisition module based on the time sequence, and synchronously feed back the retrieved friction plate use surface image data to the analysis module; Among them, the retrieval unit monitors in real time whether the analysis operation of the friction plate usage surface image data by the analysis module is completed. After the analysis of the friction plate usage surface image data is completed, another set of friction plate usage surface image data is retrieved and fed back to the analysis module again until all the friction plate usage surface image data stored in the acquisition module are retrieved once.

5. The friction plate quality detection system based on machine vision according to claim 1 is characterized in that: The friction plate quality analysis logic in the analysis module is expressed as: Where: f is the friction plate quality performance value; u is the total amount of the sub-friction plate usage surface images obtained by segmenting the friction plate usage surface image; p v is the total amount of closed contour graphics contained in the surface image of the friction plate of group v; γ is the adjustment factor; d min ,d max is the maximum diameter and minimum diameter of the closed contour figure in the surface image of each sub-friction plate; N is the set of pixels in the surface image of the friction plate; g q is the gray value of the qth pixel; μ is the gray mean value of the friction plate surface image; Among them, the adjustment factor γ is 1 or -1, p v >p v+1 , then the adjustment factor γ is -1, p v ≤p v+1 , then the adjustment factor γ is 1, Table pair The larger the friction plate quality performance value f is, the better the friction plate quality is, and vice versa, the worse the friction plate quality is. Based on the above formula, the friction plate quality performance value is obtained for each friction plate use surface image, and the sum and average of the obtained results are further calculated to finally obtain the average result F; When the friction plate quality performance value f is obtained based on the above formula, the friction plate use surface image is segmented so that each segmented sub-friction plate use surface image has the same size and shape, and when the friction plate use surface image is segmented into sub-friction plate use surface images, the number of segmentations obeys d min With d max The larger the difference, the more divisions there are, and vice versa.

6. The friction plate quality detection system based on machine vision according to claim 1 is characterized in that: The correction operation for the friction plate quality analysis result in the correction module is: f′=F×λ′ -1 ; Where: f′ is the corrected friction plate quality performance value, that is, the corrected friction plate quality analysis result.

7. The friction plate quality detection system based on machine vision according to claim 1 is characterized in that: The feedback module is connected to the mobile computer device held by the user through a wireless network. The feedback target of the feedback module is the mobile computer device held by the user. The system end user reads the determination result received by the feedback module on the mobile computer device.

8. The friction plate quality detection system based on machine vision according to claim 1 is characterized in that: The acquisition module is interactively connected to a camera module and a ranging module via a wireless network, the acquisition module is interactively connected to an evaluation module and an analysis module via a wireless network, the analysis module is interactively connected to a traversal unit and a retrieval unit via a wireless network, the traversal unit and the retrieval unit are interactively connected to the acquisition module via a wireless network, and the analysis module is interactively connected to a correction module, a determination module and a feedback module via a wireless network.

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