An electronic product middle frame support fracture defect intelligent detection method and system

Abstract

The present application relates to a kind of electronic product middle frame support fracture defect intelligent detection method and system, the electronic product middle frame support fracture defect intelligent detection method and system design ingenious, accurate positioning, by the support region is segmented folding, based on the width and height of the extracted picture size constantly iterates, until meeting iteration termination condition ends folding algorithm, to this to constantly reduce the area of fracture defect and refine, and then improve the accuracy and detection precision of subsequent fast detection fracture defect;Then target detection is carried out to the detection area, and the detection result is obtained by separately calculating each sub-image, finally the detection result obtained by all sub-images is merged to obtain the total detection result, finally the total detection result is counteracted, improve the accuracy of detection result, obtain the health status of support material and the specific position of fracture defect, the above detection method has higher detection precision, faster detection speed, therefore has good practical value.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent detection of appearance defects, and more specifically, to an intelligent detection method and system for fracture defects in a frame bracket of an electronic product. Background Art

[0002] During the complex manufacturing process from raw materials to final bracket formation, defects such as cracks, dents, and scratches often occur on the edges of electronic product midframe brackets due to various factors such as transportation, processing, and human factors. These defects affect the overall quality of the product and reduce the user experience. Traditional bracket quality inspections are done manually, which is difficult to detect even very small cracks. As a result, there are problems such as easy missed inspections, low inspection efficiency, high labor costs, and inconsistent inspection standards. These problems do not meet the high standards of product quality required by modern intelligent production.

[0003] In recent years, as machine vision has been applied to various aspects of automated production, machine vision technology has continued to mature. Existing detection technologies generally use traditional machine vision detection algorithms, mainly including scale analysis, color extraction, contrast analysis, etc. Figure 1-Figure 3 Shown are enlarged images of the left side fracture of the electronic equipment bracket, enlarged images of fracture 1 on the right side, and enlarged images of fracture 2 on the right side. However, these algorithms have high requirements for the objects being inspected and are highly dependent on them. They also have problems such as low detection efficiency, high false detection rate, and the need for targeted processing of different targets. They can detect more obvious fractures, but it is difficult to achieve good results for materials with slight cracks or overly small fractures. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method and system for intelligently detecting fracture defects in a frame bracket of an electronic product in response to the above-mentioned defects in the prior art.

[0005] On the one hand, the technical solution adopted by the present invention to solve its technical problem is: an intelligent detection method for the fracture defect of the frame bracket of an electronic product, wherein the intelligent detection method includes the following steps:

[0006] The camera captures the stent image in real time, selects the appropriate stent image as the detection template image, delineates the area where the image defect occurs, and extracts all the contours in the area. The required feature contours are then screened out, and multiple image detection templates are created based on the multiple feature contours at different locations in the image.

[0007] Find the feature contour in the image to be detected and match the image to be detected with the corresponding image detection template. If the matching score is high and the contour match is correct, the affine matrix of the detection image is calculated using the affine transformation algorithm. Otherwise, the detection area is redefined.

[0008] The folding algorithm is used to cut and fold the original material image N times. The trained fracture detection model is used to perform fracture detection on the multiple sub-images after cutting and folding to determine whether there are fracture defects in the sub-images after cutting and folding. If there are fracture defects, the location of the fracture defects is marked. If not, a material OK signal is output.

[0009] The image detection results are summarized and reverse-calculated, and all sub-images are reassembled into the original image according to the above cutting process. The health status of the stent material and the specific location of the fracture defect can be quickly detected through the marking information in the spliced ​​image;

[0010] The intelligent detection method for the fracture defect of the frame bracket of an electronic product disclosed in the present invention, wherein the method for screening characteristic contours comprises the following steps: controlling the sensitivity of the image contour outer edge detection by adjusting the filter coefficient and upper and lower limits of the filter, thereby highlighting the potential contours and extracting all contours within the fracture area; further screening the desired characteristic contours by adjusting the upper and lower limits of the contour length in the contour screening;

[0011] In the intelligent detection method for the fracture defect of the frame bracket of an electronic product described in the present invention, before delineating the image defect generation area, single-point positioning is used to select the characteristic contour of a certain position in the detection template image, and the image detection template is created based on this contour;

[0012] In the intelligent detection method for the fracture defect of the frame bracket of an electronic product according to the present invention, the folding algorithm logic is as follows:

[0013] The width and height of the material image are w and h respectively. If the width value w of the material image is greater than the height value h, the material image is cut into two equal parts at w / 2 with the height value h as the folding basis. The cut sub-image is folded along the width value w. The width and height of the folded new image are w / 2 and 2h respectively. If 2h ≥ w, the folding algorithm is exited. Otherwise, it is repeated until 2h ≥ w.

[0014] The present invention provides an intelligent method for detecting fracture defects in a frame support of an electronic product. If a fracture defect exists in a cut and folded image, the specific location of the fracture defect is marked with a frame, and a material NG signal is output simultaneously.

[0015] The present invention provides an intelligent method for detecting fracture defects in a mid-frame bracket of an electronic product, wherein the fracture detection method comprises inputting the cut and folded sub-image and the affine-transformed image of the material to be detected into a fracture defect detection model of a detection device for comparative analysis;

[0016] The intelligent detection method for the fracture defect of the frame bracket of an electronic product according to the present invention further comprises:

[0017] Based on the detection results of all sub-images, the location of the material fracture defect and the material status are presented on the original image of the detection device. At the same time, the area size of the fracture defect, the defect location coordinates, the number of fracture defects and the detection time are displayed on the data display interface;

[0018] In another aspect, the present invention provides an intelligent detection system for a broken frame support of an electronic product, comprising:

[0019] The scanning and extraction module is used to acquire the stent image in real time through the camera, select the appropriate stent image as the detection template image, delineate the area where the image defect occurs, and extract all the contours in the area, from which the required feature contours are screened;

[0020] A creation module is used to create multiple image detection templates based on multiple feature contours at different locations of the image;

[0021] The contour matching module is used to find the characteristic contour in the image to be detected and match the image to be detected with the corresponding image detection template. If the matching score is high and the contour matching is correct, the affine matrix of the detection image is calculated using the affine transformation algorithm. Otherwise, the detection area is redefined;

[0022] The fracture detection module is used to cut and fold the original material image N times; the trained fracture detection model is used to perform fracture detection on multiple sub-images after cutting and folding to determine whether there are fracture defects in the sub-images after cutting and folding;

[0023] Marking module, used to mark the specific location of the fracture defect and output the material NG signal;

[0024] The summary and inverse calculation module is used to summarize the image detection results and reassemble all sub-images into the original image according to the above cutting process. The marking information in the spliced ​​image can be used to quickly detect the health status of the stent material and the specific location of the fracture defect;

[0025] The display module is used to present the detection results of all sub-images, including the location of the material fracture defect and the material status, on the original image of the detection equipment. At the same time, the area size of the fracture defect, the defect location coordinates, the number of fracture defects and the detection time are displayed on the data display interface.

[0026] The beneficial effects of the present invention are as follows: the intelligent detection method and system design of the fracture defect of the frame bracket of the electronic product are ingenious. By extracting the characteristic contours of different positions, multiple image detection templates are created. After the image to be detected is collected, it is necessary to find the characteristic contour in the corresponding area of ​​the material to be detected according to the existing template, so as to perform template matching. Then, the material to be detected is affine transformed to ensure that the material to be detected is in the detection center and accurately positioned. By segmenting and folding the bracket area, it is continuously iterated based on the width and height of the extracted image until the folding algorithm is terminated when the iteration termination condition is met, so as to continuously reduce and refine the area where the fracture defect is located, thereby improving the accuracy and detection accuracy of subsequent rapid detection of fracture defects; then target detection is performed on the detection area, and the detection result is calculated separately for each sub-image. Finally, the detection results obtained by all sub-images are merged to obtain the total detection result. Finally, the total detection result is back-calculated to improve the accuracy of the detection result, and the health status of the bracket material and the specific location of the fracture defect are obtained. The above detection method has higher detection accuracy and faster detection speed, and therefore has good practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the present invention will be further described below with reference to the accompanying drawings and embodiments. The drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive efforts.

[0028] Figure 1 This is an enlarged view of the fracture on the left side of the electronic device bracket in the background art;

[0029] Figure 2 This is an enlarged view of the fracture 1 on the right side of the electronic device bracket in the background art;

[0030] Figure 3 This is an enlarged view of the fracture 2 on the right side of the electronic device bracket in the background art;

[0031] Figure 4 This is a flow chart of an intelligent detection method for a fracture defect of a frame bracket in an electronic product according to a first embodiment of the present invention;

[0032] Figure 5 This is a partial diagram of the detection result of the first embodiment of the present invention, taking the fracture of the left side of the electronic device bracket as an example;

[0033] Figure 6 This is a schematic diagram of an intelligent detection system for fracture defects in a frame bracket of an electronic product according to a second embodiment of the present invention. DETAILED DESCRIPTION

[0034] The terms "first," "second," "third," and "fourth," etc., in the specification, claims, and accompanying drawings of the present invention are used to distinguish between different items, not to describe a specific order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements, but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0035] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0036] "Multiple" refers to two or more. "And / or" describes the relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone. The character " / " generally indicates that the associated objects are in an "or" relationship.

[0037] Moreover, the terms "up, down, front, back, left, right, upper end, lower end, longitudinal" and the like indicating directions are all based on the posture and position of the device or apparatus described in this solution during normal use.

[0038] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the following will be a clear and complete description of the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work shall fall within the scope of protection of the present invention.

[0039] 1. Example 1

[0040] A preferred embodiment of the present invention provides an intelligent detection method for a broken frame support of an electronic product, such as Figure 4 As shown, the intelligent detection method includes the following steps:

[0041] S10: Acquire a stent image in real time through a camera, select a suitable stent image as a detection template image, delineate the image defect region and extract all contours in the region, filter out the required feature contours, and then create multiple image detection templates based on multiple feature contours at different positions in the image;

[0042] Among them, a suitable bracket image is a picture with a correct position and a clear image; the method for screening characteristic contours in the present invention includes the following steps: by adjusting the filter coefficient and the upper and lower limits of the filter to control the sensitivity of the image contour outer edge detection, the purpose of smoothing the image and reducing noise interference can be achieved, thereby highlighting the potential contour and extracting all contours in the fracture area; by adjusting the upper and lower limits of the contour length in the contour screening, the required characteristic contours are further screened out.

[0043] Alternatively, an edge detection algorithm in the prior art (such as the Sobel operator, the Canny operator, etc.) may be used to detect edges in the image, and then complete contour lines may be extracted from the edge detection results.

[0044] Furthermore, in order to quickly find out the fracture defects, before demarcating the image defect generation area, the present invention uses single-point positioning to select the characteristic contour of any position in the detection template image, and creates an image detection template based on this contour. By performing block detection on the defect area, small fracture defects can be well detected.

[0045] After collecting the image to be tested, it is necessary to find the feature contour in the corresponding area of ​​the material to be tested based on the existing template, so as to perform template matching and perform affine transformation to ensure that the material to be tested is in the center of the test.

[0046] S20: Find the characteristic contour in the image to be tested and match the image to be tested with the corresponding image detection template. If the matching score is high and the contour matching is correct, the affine matrix calculation is performed on the test image through the affine transformation algorithm to ensure that the test material is in the test center position and the positioning is accurate, thereby improving the accuracy of subsequent fracture detection; otherwise, the test area is redefined;

[0047] When extracting the ROI area (region of interest) of the middle frame bracket, the bracket area is first segmented and folded as follows:

[0048] S30: Using a folding algorithm, the original material image is cut and folded N times; using a trained fracture detection model, multiple sub-images after cutting and folding are detected for fracture one by one to determine whether the sub-images after cutting and folding have fracture defects; if so, the location of the fracture defect is marked; if not, a material OK signal is output;

[0049] The folding algorithm logic of the present invention is as follows:

[0050] The width and height of the material image are w and h respectively. If the width value w of the material image is greater than the height value h, the height value h is used as the folding basis, and the material image is cut into two equal parts at w / 2. The cut sub-image is folded along the width value w. The width and height of the folded new image are w / 2 and 2h respectively. If 2h≥w, the folding algorithm is exited; otherwise, it is iterated continuously until 2h≥w.

[0051] Based on the width and height of the extracted image, the folding algorithm is continuously iterated until the iteration termination condition is met, thereby continuously reducing and refining the area where the fracture defect is located, thereby improving the accuracy and precision of subsequent rapid detection of fracture defects and providing a better user experience.

[0052] By performing target detection on the detection area in each sub-image in the above-mentioned targeted manner, the detection result can be calculated separately for each sub-image, such as Figure 5 As shown, a partial diagram of the detection results is shown, taking the fracture on the left side of the electronic equipment bracket as an example, which improves the detection accuracy and avoids excessive external interference factors that reduce the detection rate.

[0053] In the detection results, if there is a break defect in the cut and folded image, the specific location of the break defect is marked with a frame, and the material NG signal is output at the same time; if no break defect is detected, then the material OK signal is output. There is no need to manually mark the status of the bracket material (OK or NG), saving time and effort.

[0054] S40: Summarize and reversely calculate the image detection results, and reassemble all sub-images into the original image according to the above-mentioned cutting process. The health status of the stent material and the specific location of the fracture defect can be quickly detected through the marking information in the spliced ​​image; by summarizing all the detection results and reversely splicing all the sub-images into the original image, not only can re-inspection be performed to improve the accuracy of the detection results, but the size of the image can also be accurately controlled.

[0055] S50: Based on the detection results of all sub-images, the location of the material fracture defect and the material state are presented on the original image of the detection device. At the same time, the area size of the fracture defect, the defect location coordinates, the number of fracture defects and the detection time are displayed on the data display interface. The detection results are intuitive and clear.

[0056] The intelligent detection method and system design of the fracture defect of the frame bracket of the electronic product are ingenious. By extracting the characteristic contours at different positions, multiple image detection templates are created. After the image to be detected is collected, the characteristic contours need to be found in the corresponding area of ​​the material to be detected based on the existing template to perform template matching. Then, an affine transformation is performed on the material to be detected to ensure that the material to be detected is in the detection center and accurately positioned. By segmenting and folding the bracket area, the algorithm is continuously iterated based on the width and height of the extracted image until the folding algorithm ends when the iteration termination condition is met, thereby continuously reducing and refining the area where the fracture defect is located, thereby improving the accuracy and detection precision of subsequent rapid detection of fracture defects. Then, target detection is performed on the detection area, and the detection result is calculated for each sub-image separately. Finally, the detection results obtained from all sub-images are merged to obtain the total detection result. Finally, the total detection result is back-calculated to improve the accuracy of the detection result, and the health status of the bracket material and the specific location of the fracture defect are obtained. The above detection method has higher detection accuracy and faster detection speed, and therefore has good practical value.

[0057] 2. Example 2:

[0058] The present invention also provides an intelligent detection system for the fracture defect of the frame bracket of an electronic product, which is applicable to the intelligent detection method for the fracture defect of the frame bracket of an electronic product as described in the first embodiment; Figure 6 As shown, it includes the following modules:

[0059] The scanning and extraction module is used to acquire the stent image in real time through the camera, select the appropriate stent image as the detection template image, delineate the area where the image defect occurs, and extract all the contours in the area, from which the required feature contours are screened;

[0060] A creation module is used to create multiple image detection templates based on multiple feature contours at different locations of the image;

[0061] The contour matching module is used to find the characteristic contour in the image to be detected and match the image to be detected with the corresponding image detection template. If the matching score is high and the contour matching is correct, the affine matrix calculation is performed on the detection image through the affine transformation algorithm. Otherwise, the detection area is redefined;

[0062] The fracture detection module is used to cut and fold the original material image N times; the trained fracture detection model is used to perform fracture detection on multiple sub-images after cutting and folding to determine whether there are fracture defects in the sub-images after cutting and folding;

[0063] Marking module, used to mark the specific location of the fracture defect and output the material NG signal;

[0064] The summary and inverse calculation module is used to summarize the image detection results and reassemble all sub-images into the original image according to the above cutting process. The marking information in the spliced ​​image can be used to quickly detect the health status of the stent material and the specific location of the fracture defect;

[0065] The display module is used to present the detection results of all sub-images, including the location of the material fracture defect and the material status, on the original image of the detection equipment. At the same time, the area size of the fracture defect, the defect location coordinates, the number of fracture defects and the detection time are displayed on the data display interface.

[0066] The intelligent detection method and system design of the fracture defect of the frame bracket of the electronic product are ingenious. By extracting the characteristic contours at different positions, multiple image detection templates are created. After the image to be detected is collected, the characteristic contours need to be found in the corresponding area of ​​the material to be detected based on the existing template to perform template matching. Then, an affine transformation is performed on the material to be detected to ensure that the material to be detected is in the detection center and accurately positioned. By segmenting and folding the bracket area, the algorithm is continuously iterated based on the width and height of the extracted image until the folding algorithm ends when the iteration termination condition is met, thereby continuously reducing and refining the area where the fracture defect is located, thereby improving the accuracy and detection precision of subsequent rapid detection of fracture defects. Then, target detection is performed on the detection area, and the detection result is calculated for each sub-image separately. Finally, the detection results obtained from all sub-images are merged to obtain the total detection result. Finally, the total detection result is back-calculated to improve the accuracy of the detection result, and the health status of the bracket material and the specific location of the fracture defect are obtained. The above detection method has higher detection accuracy and faster detection speed, and therefore has good practical value.

[0067] It should be understood that those skilled in the art can make improvements or changes based on the above description, and all such improvements and changes should fall within the scope of protection of the appended claims of the present invention.

Claims

1. An intelligent detection method for fracture defects of frame brackets in electronic products, characterized in that: The intelligent detection method comprises the following steps: The camera captures the stent image in real time, selects the appropriate stent image as the detection template image, delineates the area where the image defect occurs, and extracts all the contours in the area. The required feature contours are then screened out, and multiple image detection templates are created based on the multiple feature contours at different locations in the image. Find the feature contour in the image to be detected and match the image to be detected with the corresponding image detection template. If the matching score is high and the contour match is correct, the affine matrix of the detection image is calculated using the affine transformation algorithm. Otherwise, the detection area is redefined. The folding algorithm is used to cut and fold the original material image N times. The trained fracture detection model is used to perform fracture detection on the multiple sub-images after cutting and folding to determine whether there are fracture defects in the sub-images after cutting and folding. If there are fracture defects, the location of the fracture defects is marked. If not, a material OK signal is output. The image detection results are summarized and reverse-calculated, and all sub-images are reassembled into the original image according to the above cutting process. The health status of the stent material and the specific location of the fracture defect can be quickly detected through the marking information in the spliced ​​image; Based on the detection results of all sub-images, the location of the material fracture defect and the material status are presented on the original image of the detection equipment. At the same time, the area size of the fracture defect, the defect location coordinates, the number of fracture defects and the detection time are displayed on the data display interface.

2. The intelligent detection method for fracture defects of the frame support of electronic products according to claim 1, characterized in that: The method for screening characteristic contours includes the following steps: controlling the sensitivity of image contour outer edge detection by adjusting the filter coefficient and upper and lower limits of the filter, highlighting the potential contours and thus extracting all contours in the fracture area; and further screening the required characteristic contours by adjusting the upper and lower limits of the contour length in contour screening.

3. The intelligent detection method for fracture defects of the frame support of electronic products according to claim 2, characterized in that: Before delineating the area where image defects occur, single-point positioning is used to select the characteristic contour of a certain position in the detection template image, and the image detection template is created based on this contour.

4. The intelligent detection method for fracture defects of the frame support of electronic products according to claim 1, characterized in that: The folding algorithm logic is as follows: The width and height of the material image are w and h respectively. If the width value w of the material image is greater than the height value h, the height value h is used as the folding basis, and the material image is cut into two equal parts at w / 2. The cut sub-images are folded along the width value w. The width and height of the folded new images are w / 2 and 2h respectively. If Jump out of the folding algorithm, otherwise continue to iterate until .

5. The intelligent detection method for fracture defects of the frame support of an electronic product according to any one of claims 1 to 4, characterized in that: If there is a break defect in the cut and folded image, the specific location of the break defect will be marked with a frame and a material NG signal will be output at the same time.

6. The intelligent detection method for fracture defects of the frame support of electronic products according to claim 5, characterized in that: The method for detecting fracture defects includes inputting the cut and folded sub-image and the image of the material to be detected after affine transformation into the fracture defect detection model of the detection equipment for comparative analysis.

7. An intelligent detection system for fracture defects of frame brackets in electronic products, characterized in that: include: The scanning and extraction module is used to acquire the stent image in real time through the camera, select the appropriate stent image as the detection template image, delineate the area where the image defect occurs, and extract all the contours in the area, from which the required feature contours are screened; A creation module is used to create multiple image detection templates based on multiple feature contours at different locations of the image; The contour matching module is used to find the characteristic contour in the image to be detected and match the image to be detected with the corresponding image detection template. If the matching score is high and the contour matching is correct, the affine matrix calculation is performed on the detection image through the affine transformation algorithm. Otherwise, the detection area is redefined; The fracture detection module is used to cut and fold the original material image N times; the trained fracture detection model is used to perform fracture detection on multiple sub-images after cutting and folding to determine whether there are fracture defects in the sub-images after cutting and folding; Marking module, used to mark the specific location of the fracture defect and output the material NG signal; The summary and inverse calculation module is used to summarize and inversely calculate the image detection results and reassemble all sub-images into the original image according to the above-mentioned cutting process. The marking information in the spliced ​​image can be used to quickly detect the health status of the stent material and the specific location of the fracture defect; The display module is used to present the detection results of all sub-images, including the location of the material fracture defect and the material status, on the original image of the detection equipment. At the same time, the area size of the fracture defect, the defect location coordinates, the number of fracture defects and the detection time are displayed on the data display interface.

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