Inductance product packaging detection system and method

Abstract

The invention relates to the technical field of semiconductor manufacturing visual inspection, in particular to an inductance product packaging detection system and method.The system comprises packaging detection equipment and a visual detection device, the packaging detection equipment is provided with a first station and a second station, and an inductance product is fed and positioned at the first station; the system further comprises a control module, contour feature data and texture feature data are obtained according to the original image at the first station, positioning control is carried out, pixel statistics and comparative analysis are carried out on the packaged image based on the feature data, and packaging defect detection and blanking are carried out at the second station. And obtaining a packaging defect detection result and carrying out blanking indication control. Compared with the prior art, the method has the advantages that the technological characteristics of an inductance product between packaging double stations are creatively utilized, efficient and light-weight defect detection is achieved through a characteristic data cross-station multiplexing mechanism, the image processing data size is greatly reduced, and the problem that in the prior art, the requirement for hardware is high when packaging detection is conducted through vision is solved.

Description

Technical Field

[0001] This invention relates to the field of visual inspection technology in semiconductor manufacturing, and in particular to a packaging inspection system and method for inductor products. Background Technology

[0002] Packaging inspection is a crucial step in the production of electronic components. Its core function is to ensure that products meet design specifications during the packaging process through high-precision measurement and defect identification. Traditional inspection methods rely on manual visual inspection or contact measuring tools, but these are limited by subjective judgment differences and measurement efficiency, making it difficult to meet the demands of modern electronic component mass production with high consistency.

[0003] With the development of machine vision technology, automated inspection solutions based on image processing have gradually become mainstream. These solutions involve acquiring product images using industrial cameras, extracting key parameters using algorithms such as edge detection and feature matching, and classifying defects using preset thresholds. Compared to traditional methods, machine vision-assisted inspection offers advantages such as non-contact measurement and suitability for large-scale inspection.

[0004] However, existing machine vision inspection technologies still face significant bottlenecks when applied to inductor packaging scenarios—their algorithms typically rely on complex deep learning models, demanding extremely high hardware computing power. For example, traditional solutions require high-performance GPUs to process high-resolution images (e.g., 5 megapixels or higher) in real time, and steps such as multi-scale feature extraction and multi-object segmentation further increase the computational load, resulting in single-frame processing times of tens of milliseconds, which is difficult to match the fast pace of production lines and also increases equipment costs. Therefore, a more lightweight vision-based packaging inspection solution is needed. Summary of the Invention

[0005] Therefore, the present invention provides a packaging inspection system and method for inductor products to solve the problem that the hardware requirements for packaging inspection using vision in the prior art are high.

[0006] This invention provides a packaging and inspection system for inductor products, including packaging and inspection equipment with a first station and a second station, and a vision inspection device. The vision inspection device is used to capture an original image of the inductor product at the first station and a packaged image of the inductor product at the second station. The inductor product is loaded and positioned at the first station based on the original image, and after being packaged by the packaging and inspection equipment, it is inspected for packaging defects and unloaded at the second station based on the packaged image. The system also includes a control module, which includes: The contour analysis module is used to obtain the contour feature data of the inductor product based on the original image; The positioning control module is used to obtain the positioning indication data of the first station based on the contour feature data; The texture analysis module is used to obtain the texture feature data of the inductor product based on the original image; The packaging detection module is used to perform pixel statistics and comparison analysis on the packaging image based on contour feature data and texture feature data to obtain the packaging defect detection results. The detection and control module is used to obtain material unloading instruction data based on the results of packaging defect detection.

[0007] In a preferred implementation: based on the original image, the contour feature data of the inductor product is obtained, including: Gradient calculation is performed on the original image to obtain the gradient magnitude map and gradient direction map; Based on the specifications and shape of the inductor product, the directional screening range is obtained; Based on the gradient direction map, non-maximum suppression is performed within the direction filtering range to obtain a refined contour map; Based on the refined contour map, contour feature data is obtained.

[0008] In a preferred implementation: the directional filtering range is obtained based on the specifications and shape of the inductor product, including: The first range component is obtained based on the aspect ratio of the inductor product; The second range component is obtained based on the size ratio of the inductive product and the background objects in the original image; The direction filtering range is obtained by combining the first range component and the second range component.

[0009] In a preferred implementation: based on the original image, the texture feature data of the inductor product is obtained, including: Based on the contour feature data, the ROI region of the inductor product is obtained in the original image; Statistical analysis of the grayscale values ​​within the ROI region yields texture feature data.

[0010] In a preferred implementation: the encapsulation defect detection results include adhesive uniformity detection results; based on contour feature data and texture feature data, pixel statistics and comparison analysis are performed on the encapsulation image to obtain the encapsulation defect detection results, including: Obtain the encapsulated image, which contains a preset positioning center; Based on a preset positioning center, a first detection region in the encapsulated image is obtained according to contour feature data, wherein the range of the first detection region is obtained based on the contour expansion represented by the contour feature data; The grayscale values ​​of the first detection region are statistically analyzed to obtain the grayscale histogram of the first detection region; The kurtosis of the grayscale histogram is calculated, and the glue volume uniformity test result is obtained by comparing the kurtosis with the preset kurtosis threshold.

[0011] In a preferred implementation: the packaging defect detection results include bubble defect detection results; based on contour feature data and texture feature data, pixel statistics and comparison analysis are performed on the packaging image to obtain the packaging defect detection results, including: Obtain the encapsulated image, which contains a preset positioning center; Based on a preset positioning center, a second detection region in the encapsulated image is obtained according to contour feature data, wherein the range of the second detection region is obtained based on the contour expansion represented by the contour feature data; Divide the second detection area into a uniform grid, and randomly select multiple windows in each grid to obtain multiple detection windows; Statistical analysis is performed on the grayscale values ​​within each detection window to obtain the texture detection features of each detection window; The number of abnormal windows is obtained by comparing the texture detection features and texture feature data. The bubble defect detection results are obtained by comparing the number of abnormal windows with the preset number threshold.

[0012] In a preferred implementation: the packaging inspection equipment further includes a third station for dispensing and packaging inductor products; the packaging defect detection results include glue overflow detection results; based on contour feature data and texture feature data, pixel statistics and comparison analysis are performed on the packaging image to obtain the packaging defect detection results, including: Obtain the encapsulated image, which contains a preset positioning center; Based on a preset positioning center, a third detection region in the encapsulated image is obtained according to contour feature data. The range of the third detection region is obtained based on the contour expansion represented by the contour feature data. Obtain the dispensing pressure and nozzle movement speed of the third station, and obtain the glue area threshold based on the dispensing pressure and nozzle movement speed. Based on the grayscale value of the third detection region, the number of pixels in the third detection region that can characterize the adhesive area is counted. The glue overflow detection result is obtained by comparing the number of pixels and the glue area threshold.

[0013] This invention also provides a packaging and inspection method for inductor products, applied to a packaging and inspection system for inductor products. The system includes packaging and inspection equipment with a first station and a second station, and a vision inspection device. The vision inspection device is used to capture original images of the inductor product at the first station and packaged images of the inductor product at the second station. The inductor product is loaded and positioned at the first station based on the original images, packaged by the packaging and inspection equipment, and then inspected for packaging defects and unloaded at the second station based on the packaged images. The system also includes a control module for running the packaging and inspection method for inductor products, which includes: Based on the original image, the contour feature data of the inductor product is obtained; Based on the contour feature data, the positioning indication data of the first station is obtained; Based on the original image, the texture feature data of the inductor product is obtained; Based on contour feature data and texture feature data, pixel statistics and comparison analysis are performed on the packaging image to obtain the packaging defect detection results. Based on the results of the packaging defect detection, the material cutting instruction data is obtained.

[0014] The present invention also provides an electronic device, comprising: Memory and processor; The memory is used to store the program, and the processor is used to execute the steps in the above-described packaging and testing method for inductor products when the program is executed.

[0015] The present invention also provides a computer-readable storage medium for storing a computer-readable program or instructions, which, when executed by a processor, can implement the steps in the above-described packaging and testing method for inductor products.

[0016] The beneficial effects of adopting the above scheme are: This invention provides a packaging and inspection system for inductor products, including a packaging and inspection device with a first station and a second station, and a vision inspection device. The vision inspection device is used to capture an original image of the inductor product at the first station and a packaged image of the inductor product at the second station. The inductor product is loaded and positioned at the first station based on the original image. After being packaged by the packaging and inspection device, it is subjected to packaging defect detection and unloading at the second station based on the packaged image. The system also includes a control module, which includes a contour analysis module for obtaining contour feature data of the inductor product based on the original image, a positioning control module for obtaining positioning indication data of the first station based on the contour feature data, a texture analysis module for obtaining texture feature data of the inductor product based on the original image, a packaging inspection module for performing pixel statistics and comparison analysis on the packaged image based on the contour feature data and texture feature data to obtain packaging defect detection results, and a detection control module for obtaining unloading indication data based on the packaging defect detection results. Compared to existing technologies, this invention innovatively utilizes the process characteristics of inductor products between two packaging stations. Through a feature data cross-station reuse mechanism, it achieves efficient and lightweight defect detection. Specifically, the contour feature data and texture feature data extracted at the first station are directly transferred to the second station. This eliminates the need for repeated feature extraction during the second station's detection; only a rapid pixel comparison of the preset key detection areas is required. Compared to traditional full-image scanning schemes, this significantly reduces the amount of image processing data, minimizes redundant feature extraction calculations at the second station, improves detection speed, reduces algorithm complexity, and lowers hardware costs. It perfectly balances efficiency and accuracy requirements, solving the problem of high hardware requirements for vision-based packaging inspection in existing technologies. Attached Figure Description

[0017] Figure 1 This is a system architecture diagram of the packaging and testing system for inductor products provided by the present invention; Figure 2 This is a flowchart of the packaging and testing method for inductor products provided by the present invention; Figure 3 for Figure 2 The detailed steps of step S201 are shown in the diagram. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Combination Figure 1-2As shown in a specific embodiment of the present invention, a packaging and inspection system for inductor products is disclosed, including a packaging and inspection device 110 having a first station 111 and a second station 112, and a vision inspection device 120. The vision inspection device is used to capture original images of the inductor products at the first station and to capture packaged images of the inductor products at the second station. The inductor products are loaded and positioned at the first station based on the original images, and after being packaged by the packaging and inspection device, they are inspected for packaging defects and unloaded at the second station based on the packaged images. The system also includes a control module 130, which includes: The contour analysis module 131 is used to perform step S201, obtaining the contour feature data of the inductor product based on the original image; The positioning control module 132 is used to execute step S202 and obtain the positioning indication data of the first station based on the contour feature data; The texture analysis module 133 is used to perform step S203 and obtain the texture feature data of the inductor product based on the original image. The packaging detection module 134 is used to execute step S204, which performs pixel statistics and comparison analysis on the packaging image based on contour feature data and texture feature data, and obtains packaging defect detection results. The detection control module 135 is used to execute step S205 and obtain material unloading instruction data based on the packaging defect detection results.

[0020] In this embodiment, the control module mainly refers to the computer program product, while the packaging and testing equipment and visual inspection device can be implemented in any form. For example, in the working platform of a certain integrated packaging and testing automated equipment, three turntable mechanisms are set up simultaneously, namely turntable A, turntable B, and turntable C. Turntable A is equipped with a corresponding feeding mechanism, camera, and clamping mechanism such as a four-jaw chuck, and the feeding and positioning are controlled by image recognition from the camera. The positioned inductor products are transferred from turntable A to turntable B, where turntable B is equipped with a corresponding dispensing and baking mechanism for packaging the inductor products. The packaged products are transferred from turntable B to turntable C, where turntable C is equipped with a camera and suction cups, which are used to analyze packaging defects through image recognition from the camera and decide whether to unload qualified products for the next process, while unqualified products are discarded. Turntable A is the first station in this invention, turntable C is the second station in this invention, the camera is a visual inspection device, the image captured by turntable A is the original image, and the image captured by turntable C is the packaged image.

[0021] Compared to existing technologies, this invention innovatively utilizes the process characteristics of inductor products between two packaging stations. Through a cross-station feature data reuse mechanism, it achieves efficient and lightweight defect detection. Specifically, the contour feature data extracted at the first station is not only used for positioning but is also directly transferred to the second station for rapid target recognition (e.g., expanding the contour by 3mm to quickly obtain the area representing the workpiece in the packaging image). This avoids repetitive target recognition calculations during defect detection and significantly reduces the image size that needs to be processed. Simultaneously, the texture feature extraction process at the first station can be performed using the inductor product during the packaging process (such as the dispensing time of disk B in the above embodiment), fully utilizing the processing time gap between the first and second stations, perfectly forming an interleaved production line and significantly shortening the computation time. Most importantly, based on the appearance characteristics of inductor product packaging (the shape remains the same, but the size increases; depending on the color of the packaging colloid, the color of the inductor product may change before and after packaging, while the background texture of the turntable is basically the same), the second station can directly utilize texture feature data to quickly perform packaging inspection through pixel statistics and comparison analysis (i.e., non-artificial intelligence recognition). There is no need to run complex deep learning-based image recognition algorithms, which reduces algorithm complexity and hardware costs, perfectly balancing efficiency and accuracy requirements, and solving the problem of high hardware requirements for vision-based packaging inspection in existing technologies.

[0022] Combination Figure 3 In a more preferred embodiment, step S201, obtaining the contour feature data of the inductor product based on the original image, specifically includes: S301. Perform gradient calculation on the original image to obtain the gradient magnitude map and gradient direction map; S302. Based on the specifications and shape of the inductor product, obtain the directional screening range; S303. Based on the gradient direction map, non-maximum suppression is performed within the direction filtering range to obtain a refined contour map. S304. Obtain contour feature data based on the refined contour map.

[0023] The above process is an improvement on the existing Canny edge detection algorithm. In step S301, gradient calculation can be performed using methods such as the Sobel operator. In step S304, the outline of the inductor product can be quickly located based on the positional relationship between the camera and the turntable. The outline feature data is obtained by statistically analyzing the length, width, and center coordinates of the minimum bounding rectangle. These are all existing technologies that can be understood by those skilled in the art, and therefore will not be elaborated upon further. It is worth noting that steps S302 and S303 in this embodiment are improvements to the non-maximum suppression process in the existing Canny algorithm. In non-maximum suppression, the usual approach is to check the gradient direction of the current pixel, then compare the gradient magnitudes of adjacent pixels in the positive and negative directions of that direction, retaining the maximum value to complete the filtering. However, in this embodiment, the directional filtering range is obtained using the specifications and shape of the inductor product, and filtering is performed within this range instead of a single direction. This has the following beneficial effects: Improve the retention rate of inductor edges: Since inductor edges are continuous in multiple directions, while background edges are often isolated, inductor edges can be better preserved by multi-directional non-maximum suppression within a range.

[0024] Effective suppression of background edges: Background edges typically do not satisfy local maxima in multiple directions (e.g., the loneliness of a turntable compared to an inductor can be considered as a straight line), and are therefore suppressed.

[0025] Adaptive adjustment: The directional range is dynamically adjusted according to the specific specifications and shape of the inductor product, so as to maintain a high detection rate in complex backgrounds and improve positioning accuracy in simple backgrounds.

[0026] Specifically, in a preferred embodiment, step S302, obtaining the directional screening range based on the specifications and shape of the inductor product, specifically includes: The first range component is obtained based on the aspect ratio of the inductor product; The second range component is obtained based on the size ratio of the inductive product and the background objects in the original image; The direction filtering range is obtained by combining the first range component and the second range component.

[0027] Understandably, in practice, inductors typically have a major axis and a minor axis, with their edges primarily along the major axis (0 degrees) and minor axis (90 degrees), as well as the 45-degree and 135-degree directions (because inductors are usually rectangular or elliptical, with four main edge directions). Therefore, in this embodiment, during non-maximum suppression, by considering non-maximum suppression in multiple directions (such as the gradient direction θ, considering directions within the range of θ+45° to θ-45°), it can ensure that the current edge point is a local maximum in multiple directions, thus making it more likely to be an inductor edge (because inductor edges usually have continuity and regularity in different directions, while background textures often do not have this characteristic and are more random). This embodiment requires that within this range (the first range component), the gradient magnitude of the current pixel is maximized before retaining the edge point.

[0028] On the other hand, the size of background objects (such as turntables) is usually larger than that of inductors, and the change in edge direction is smaller compared to inductor products. Therefore, this embodiment also finely adjusts the background edges by setting a small angle range (i.e., the second range component, such as 5 degrees).

[0029] A formula representing the above process is as follows: ; Where Δθ is the direction filtering range, e is the natural constant, k is the preset shape sensitivity coefficient (e.g., 0.8 obtained experimentally), and R is the aspect ratio of the current inductor product. To minimize the inductor area, The area is the background. In the above formula, the first term is the first range component, which implements aspect ratio deviation penalty through the natural exponential function. The second term is the second range component, which judges the degree of background interference by the size ratio of the inductance and the background, and then corrects it to obtain a more accurate screening angle range. This improves the inductance edge retention rate in the non-maximum suppression process and reduces the background false detection rate.

[0030] The final angle range for maximum comparison is obtained based on the gradient direction θ. for: ; Furthermore, in a preferred embodiment, step S203, obtaining texture feature data of the inductor product based on the original image, specifically includes: Based on the contour feature data, the ROI region of the inductor product is obtained in the original image; Statistical analysis of the grayscale values ​​within the ROI region yields texture feature data.

[0031] In the above process, the ROI region is the area representing inductance in the original image obtained based on contour features. Texture feature data is obtained through grayscale statistical analysis (e.g., calculating the mean, variance, and contrast of the grayscale co-occurrence matrix). In this embodiment, obtaining texture features through grayscale statistics achieves position-independent texture comparison—in subsequent package detection, this texture feature data can be used as a benchmark, and the local texture of the package image can be quickly compared with this benchmark to obtain the detection result, without the need for relocation or global scanning, significantly reducing hardware costs and significantly improving efficiency.

[0032] Furthermore, in a preferred embodiment, the packaging defect detection result includes the glue uniformity detection result. Based on this, step S204 above, which involves performing pixel statistics and comparison analysis on the packaging image based on contour feature data and texture feature data, yields the packaging defect detection result, specifically including: Obtain the encapsulated image, which contains a preset positioning center; Based on a preset positioning center, a first detection region in the encapsulated image is obtained according to contour feature data, wherein the range of the first detection region is obtained based on the contour expansion represented by the contour feature data; The grayscale values ​​of the first detection region are statistically analyzed to obtain the grayscale histogram of the first detection region; The kurtosis of the grayscale histogram is calculated, and the glue volume uniformity test result is obtained by comparing the kurtosis with the preset kurtosis threshold.

[0033] In the above process, the preset positioning center can be manually preset, for example, defining the position corresponding to the center of the suction cup as the preset positioning center. The area centered on the preset positioning center is the detection area, used to represent the approximate position of the packaged inductor product in the package image. The detection area is obtained by expanding the contour represented by contour feature data (typically an expansion range of 2-3 mm). The degree of expansion can vary for different defect detection targets. This embodiment dynamically generates the first detection area based on the preset positioning center and contour feature data, achieving precise positioning of the detection area and reducing most of the invalid scanning area compared to fixed area detection methods.

[0034] Kurtosis is a concept used to measure the steepness of a probability distribution, and its calculation method is existing technology that can be understood by those skilled in the art. In this embodiment, gray-scale histogram kurtosis analysis is used as a criterion for glue quantity uniformity. By setting a kurtosis threshold, defects such as insufficient glue quantity (sharp distribution, kurtosis > 3.5) and oversaturated glue quantity (flat distribution, kurtosis < 2.5) can be identified simultaneously, with high detection sensitivity.

[0035] Furthermore, in a preferred embodiment, the packaging defect detection result includes the bubble defect detection result. Based on this, step S204 above, which involves performing pixel statistics and comparison analysis on the packaging image based on contour feature data and texture feature data, yields the packaging defect detection result, including: Obtain the encapsulated image, which contains a preset positioning center; Based on a preset positioning center, a second detection region in the encapsulated image is obtained according to contour feature data, wherein the range of the second detection region is obtained based on the contour expansion represented by the contour feature data; Divide the second detection area into a uniform grid, and randomly select multiple windows in each grid to obtain multiple detection windows; Statistical analysis is performed on the grayscale values ​​within each detection window to obtain the texture detection features of each detection window; The number of abnormal windows is obtained by comparing the texture detection features and texture feature data. The bubble defect detection results are obtained by comparing the number of abnormal windows with the preset number threshold.

[0036] It is understood that in this embodiment, the method for extracting texture features within the window is the same as that described earlier in the original image (statistical features of grayscale values, such as grayscale mean, variance, etc.), to facilitate comparison. The difference between the texture detection features and the texture feature data can be evaluated by calculating cosine similarity. The specific threshold for defect determination can also be flexibly set according to actual needs. For example, detection windows with a difference greater than 0.15 can be marked as abnormal windows. When there are more than three abnormal windows, it can be determined that there is a bubble defect.

[0037] This embodiment constructs a multi-scale detection system by uniform grid division (such as 3×3 grid) and random window sampling (5 5×5 pixel windows per grid), which eliminates the need for precise bubble location identification and reduces most of the computational load compared to traditional full-area scanning, greatly reducing the required hardware level.

[0038] Furthermore, in a preferred embodiment, the packaging inspection equipment further includes a third station for dispensing and packaging inductor products; the packaging defect detection results include adhesive overflow detection results. Based on this, in step S204 above, based on contour feature data and texture feature data, pixel statistics and comparison analysis are performed on the packaging image to obtain packaging defect detection results, including: Obtain the encapsulated image, which contains a preset positioning center; Based on a preset positioning center, a third detection region in the encapsulated image is obtained according to contour feature data. The range of the third detection region is obtained based on the contour expansion represented by the contour feature data. Obtain the dispensing pressure and nozzle movement speed of the third station, and obtain the glue area threshold based on the dispensing pressure and nozzle movement speed. Based on the grayscale value of the third detection region, the number of pixels in the third detection region that can characterize the adhesive area is counted. The glue overflow detection result is obtained by comparing the number of pixels and the glue area threshold.

[0039] In the above process, the third station is the dispensing station located between the first and second stations, such as disk B in the previous embodiment. The outer range of the third detection area is larger than that of the first and second detection areas.

[0040] A specific example of obtaining the adhesive area threshold is as follows: ; in, is the glue area threshold, w is the preset glue diffusion coefficient, such as 0.18 obtained from experience, P is the dispensing pressure of the third station, V is the nozzle movement speed of the third station, and b is the preset adjustment coefficient, such as 20 obtained from experience.

[0041] Understandably, the method for determining the pixels representing the adhesive area in the above process can be designed according to the specific type of adhesive. For example, if the encapsulating adhesive is black and the background turntable is silver, then the pixels with larger gray values ​​in the third detection area (such as gray values ​​greater than the mean + 20) are the pixels representing the adhesive area.

[0042] This embodiment creatively couples the real-time dispensing parameters of the third station with the visual inspection depth, generating an adaptive area threshold through a dynamic glue volume model, which greatly improves adaptability. Most importantly, this embodiment, assisted by process parameters, effectively overcomes the instability problem of relying solely on image visual inspection, improves detection accuracy, ensures high-speed processing performance, and perfectly solves the problem of inspection stability caused by environmental fluctuations in mass production.

[0043] It is worth noting that none of the three defect detection methods mentioned above require the use of complex artificial intelligence vision algorithms. They only need to perform simple statistical analysis and comparison of pixel grayscale values ​​to achieve defect judgment, which greatly reduces complexity and hardware requirements.

[0044] Please combine Figure 2The present invention also provides a packaging and inspection method for inductor products, applied to a packaging and inspection system for inductor products. The system includes packaging and inspection equipment with a first station and a second station, and a vision inspection device. The vision inspection device is used to capture original images of the inductor product at the first station and packaged images of the inductor product at the second station. The inductor product is loaded and positioned at the first station based on the original images, and after being packaged by the packaging and inspection equipment, it is inspected for packaging defects and unloaded at the second station based on the packaged images. The system also includes a control module for running the packaging and inspection method for inductor products, the method comprising: S201. Obtain the contour feature data of the inductor product based on the original image; S202. Based on the contour feature data, obtain the positioning indication data for the first station; S203. Obtain the texture feature data of the inductor product based on the original image; S204. Based on contour feature data and texture feature data, perform pixel statistics and comparison analysis on the packaging image to obtain the packaging defect detection results. S205. Based on the packaging defect detection results, obtain the material unloading instruction data.

[0045] The present invention also provides an electronic device, comprising: Memory and processor; The memory is used to store the program, and the processor is used to execute the steps in the above-described packaging and testing method for inductor products when the program is executed.

[0046] The present invention also provides a computer-readable storage medium for storing a computer-readable program or instructions, which, when executed by a processor, can implement the steps in the above-described packaging and testing method for inductor products.

[0047] This invention provides a packaging and inspection system for inductor products, including a packaging and inspection device with a first station and a second station, and a vision inspection device. The vision inspection device is used to capture an original image of the inductor product at the first station and a packaged image of the inductor product at the second station. The inductor product is loaded and positioned at the first station based on the original image. After being packaged by the packaging and inspection device, it is subjected to packaging defect detection and unloading at the second station based on the packaged image. The system also includes a control module, which includes a contour analysis module for obtaining contour feature data of the inductor product based on the original image, a positioning control module for obtaining positioning indication data of the first station based on the contour feature data, a texture analysis module for obtaining texture feature data of the inductor product based on the original image, a packaging inspection module for performing pixel statistics and comparison analysis on the packaged image based on the contour feature data and texture feature data to obtain packaging defect detection results, and a detection control module for obtaining unloading indication data based on the packaging defect detection results. Compared to existing technologies, this invention innovatively utilizes the process characteristics of inductor products between two packaging stations. Through a feature data cross-station reuse mechanism, it achieves efficient and lightweight defect detection. Specifically, the contour feature data and texture feature data extracted at the first station are directly transferred to the second station. This eliminates the need for repeated feature extraction during the second station's detection; only a rapid pixel comparison of the preset key detection areas is required. Compared to traditional full-image scanning schemes, this significantly reduces the amount of image processing data, minimizes redundant feature extraction calculations at the second station, improves detection speed, reduces algorithm complexity, and lowers hardware costs. It perfectly balances efficiency and accuracy requirements, solving the problem of high hardware requirements for vision-based packaging inspection in existing technologies.

[0048] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0049] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A packaging and testing system for inductor products, characterized in that, The system includes packaging and inspection equipment with a first and a second workstation, as well as a vision inspection device. The vision inspection device is used to capture original images of the inductor products at the first workstation and packaged images of the inductor products at the second workstation. The inductor products are loaded and positioned at the first workstation based on the original images. After being packaged by the packaging and inspection equipment, they are inspected for packaging defects and unloaded at the second workstation based on the packaged images. The system also includes a control module, which includes: The contour analysis module is used to obtain the contour feature data of the inductor product based on the original image; The positioning control module is used to obtain the positioning indication data of the first station based on the contour feature data; The texture analysis module is used to obtain the texture feature data of the inductor product based on the original image; The packaging detection module is used to perform pixel statistics and comparison analysis on the packaging image based on contour feature data and texture feature data to obtain the packaging defect detection results. The detection and control module is used to obtain material unloading instruction data based on the results of packaging defect detection.

2. The packaging and testing system for inductor products according to claim 1, characterized in that, Based on the original image, the contour feature data of the inductor product is obtained, including: Gradient calculation is performed on the original image to obtain the gradient magnitude map and gradient direction map; Based on the specifications and shape of the inductor product, the directional screening range is obtained; Based on the gradient direction map, non-maximum suppression is performed within the direction filtering range to obtain a refined contour map; Based on the refined contour map, contour feature data is obtained.

3. The packaging and testing system for inductor products according to claim 2, characterized in that, Based on the specifications and shape of the inductor product, the directional screening range is obtained, including: The first range component is obtained based on the aspect ratio of the inductor product; The second range component is obtained based on the size ratio of the inductive product and the background objects in the original image; The direction filtering range is obtained by combining the first range component and the second range component.

4. The packaging and testing system for inductor products according to claim 1, characterized in that, Based on the original image, the texture feature data of the inductor product is obtained, including: Based on the contour feature data, the ROI region of the inductor product is obtained in the original image; Statistical analysis of the grayscale values ​​within the ROI region yields texture feature data.

5. The packaging and testing system for inductor products according to claim 1, characterized in that, The results of the encapsulation defect detection include the results of the glue uniformity detection; Based on contour feature data and texture feature data, pixel statistics and comparison analysis are performed on the packaged image to obtain the packaged defect detection results, including: Obtain the encapsulated image, which contains a preset positioning center; Based on a preset positioning center, a first detection region in the encapsulated image is obtained according to contour feature data, wherein the range of the first detection region is obtained based on the contour expansion represented by the contour feature data; The grayscale values ​​of the first detection region are statistically analyzed to obtain the grayscale histogram of the first detection region; The kurtosis of the grayscale histogram is calculated, and the glue volume uniformity test result is obtained by comparing the kurtosis with the preset kurtosis threshold.

6. The packaging and testing system for inductor products according to claim 1, characterized in that, The results of the packaging defect detection include the results of the bubble defect detection; Based on contour feature data and texture feature data, pixel statistics and comparison analysis are performed on the packaged image to obtain the packaged defect detection results, including: Obtain the encapsulated image, which contains a preset positioning center; Based on a preset positioning center, a second detection region in the encapsulated image is obtained according to contour feature data, wherein the range of the second detection region is obtained based on the contour expansion represented by the contour feature data; Divide the second detection area into a uniform grid, and randomly select multiple windows in each grid to obtain multiple detection windows; Statistical analysis is performed on the grayscale values ​​within each detection window to obtain the texture detection features of each detection window; The number of abnormal windows is obtained by comparing the texture detection features and texture feature data. The bubble defect detection results are obtained by comparing the number of abnormal windows with the preset number threshold.

7. The packaging and testing system for inductor products according to claim 1, characterized in that, The packaging and testing equipment also includes a third station for dispensing and packaging inductor products; the packaging defect detection results include the results of glue overflow detection. Based on contour feature data and texture feature data, pixel statistics and comparison analysis are performed on the packaged image to obtain the packaged defect detection results, including: Obtain the encapsulated image, which contains a preset positioning center; Based on a preset positioning center, a third detection region in the encapsulated image is obtained according to contour feature data. The range of the third detection region is obtained based on the contour expansion represented by the contour feature data. Obtain the dispensing pressure and nozzle movement speed of the third station, and obtain the glue area threshold based on the dispensing pressure and nozzle movement speed. Based on the grayscale value of the third detection region, the number of pixels in the third detection region that can characterize the adhesive area is counted. The glue overflow detection result is obtained by comparing the number of pixels and the glue area threshold.

8. A packaging and testing method for an inductor product, characterized in that, A packaging and inspection system for inductor products includes packaging and inspection equipment with a first station and a second station, and a vision inspection device. The vision inspection device captures original images of the inductor products at the first station and packaged images of the inductor products at the second station. The inductor products are loaded and positioned at the first station based on the original images. After being packaged by the packaging and inspection equipment, they are inspected for packaging defects and unloaded at the second station based on the packaged images. The system also includes a control module for running a packaging and inspection method for the inductor products, which includes: Based on the original image, the contour feature data of the inductor product is obtained; Based on the contour feature data, the positioning indication data of the first station is obtained; Based on the original image, the texture feature data of the inductor product is obtained; Based on contour feature data and texture feature data, pixel statistics and comparison analysis are performed on the packaging image to obtain the packaging defect detection results. Based on the results of the packaging defect detection, the material cutting instruction data is obtained.

9. An electronic device, characterized in that, include: Memory and processor; The memory is used to store the program, and the processor is used to execute the steps in the packaging and testing method for the inductor product of claim 8 when the program is executed.

10. A computer-readable storage medium, characterized in that, Used to store computer-readable programs or instructions, which, when executed by a processor, can implement the steps in the packaging and testing method for inductor products according to claim 8.

Images