Research on PCB defect detection algorithm based on LPCB-YOLO
IntroductionIn response to the challenges of small target size, slow detection speed, and large model parameters in PCB surface defect detection, LPCB-YOLO was designed. The goal was to ensure detection accuracy and comprehensiveness while significantly reducing model parameters and improving comput...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Physics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2024.1472584/full |
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| author | Haiyan Zhang Haiyan Zhang Yazhou Li Yazhou Li Dipu Md Sharid Kayes Dipu Md Sharid Kayes Zhaoyu Song Zhaoyu Song Yuanyuan Wang Yuanyuan Wang |
| author_facet | Haiyan Zhang Haiyan Zhang Yazhou Li Yazhou Li Dipu Md Sharid Kayes Dipu Md Sharid Kayes Zhaoyu Song Zhaoyu Song Yuanyuan Wang Yuanyuan Wang |
| author_sort | Haiyan Zhang |
| collection | DOAJ |
| description | IntroductionIn response to the challenges of small target size, slow detection speed, and large model parameters in PCB surface defect detection, LPCB-YOLO was designed. The goal was to ensure detection accuracy and comprehensiveness while significantly reducing model parameters and improving computational speed.MethodFirst, the feature extraction networks consist of multiple CSPELAN modules for feature extraction of small target defects on PCBs. This allows for sufficient feature representation while greatly reducing the number of model parameters. Second, the C-SPPF module enables the fusion of high-level semantic expression with low-level feature layers to enhance global feature perception capability, improving the overall contextual expression of the backbone and thereby enhancing model performance. Finally, the C2f-GS module is designed to fuse high-level semantic features and low-level detail features to enhance the feature representation capability and model performance.ResultsThe experimental results show that the LPCB-YOLO model reduces the model size by 24% compared to that of the YOLOv8 model while maintaining high precision and recall at 97.0%. |
| format | Article |
| id | doaj-art-ab65d8977bcb4656ba2d95ac0f8014fb |
| institution | Kabale University |
| issn | 2296-424X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physics |
| spelling | doaj-art-ab65d8977bcb4656ba2d95ac0f8014fb2025-01-03T05:10:22ZengFrontiers Media S.A.Frontiers in Physics2296-424X2025-01-011210.3389/fphy.2024.14725841472584Research on PCB defect detection algorithm based on LPCB-YOLOHaiyan Zhang0Haiyan Zhang1Yazhou Li2Yazhou Li3Dipu Md Sharid Kayes4Dipu Md Sharid Kayes5Zhaoyu Song6Zhaoyu Song7Yuanyuan Wang8Yuanyuan Wang9College of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, ChinaLaboratory for Internet of Things and Mobile Internet Technology of Jiangsu Province, Huaian, ChinaCollege of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, ChinaLaboratory for Internet of Things and Mobile Internet Technology of Jiangsu Province, Huaian, ChinaCollege of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, ChinaLaboratory for Internet of Things and Mobile Internet Technology of Jiangsu Province, Huaian, ChinaCollege of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, ChinaLaboratory for Internet of Things and Mobile Internet Technology of Jiangsu Province, Huaian, ChinaCollege of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, ChinaLaboratory for Internet of Things and Mobile Internet Technology of Jiangsu Province, Huaian, ChinaIntroductionIn response to the challenges of small target size, slow detection speed, and large model parameters in PCB surface defect detection, LPCB-YOLO was designed. The goal was to ensure detection accuracy and comprehensiveness while significantly reducing model parameters and improving computational speed.MethodFirst, the feature extraction networks consist of multiple CSPELAN modules for feature extraction of small target defects on PCBs. This allows for sufficient feature representation while greatly reducing the number of model parameters. Second, the C-SPPF module enables the fusion of high-level semantic expression with low-level feature layers to enhance global feature perception capability, improving the overall contextual expression of the backbone and thereby enhancing model performance. Finally, the C2f-GS module is designed to fuse high-level semantic features and low-level detail features to enhance the feature representation capability and model performance.ResultsThe experimental results show that the LPCB-YOLO model reduces the model size by 24% compared to that of the YOLOv8 model while maintaining high precision and recall at 97.0%.https://www.frontiersin.org/articles/10.3389/fphy.2024.1472584/fullprinted circuit boardlightweight networkELANdefects detectiontiny target detection |
| spellingShingle | Haiyan Zhang Haiyan Zhang Yazhou Li Yazhou Li Dipu Md Sharid Kayes Dipu Md Sharid Kayes Zhaoyu Song Zhaoyu Song Yuanyuan Wang Yuanyuan Wang Research on PCB defect detection algorithm based on LPCB-YOLO Frontiers in Physics printed circuit board lightweight network ELAN defects detection tiny target detection |
| title | Research on PCB defect detection algorithm based on LPCB-YOLO |
| title_full | Research on PCB defect detection algorithm based on LPCB-YOLO |
| title_fullStr | Research on PCB defect detection algorithm based on LPCB-YOLO |
| title_full_unstemmed | Research on PCB defect detection algorithm based on LPCB-YOLO |
| title_short | Research on PCB defect detection algorithm based on LPCB-YOLO |
| title_sort | research on pcb defect detection algorithm based on lpcb yolo |
| topic | printed circuit board lightweight network ELAN defects detection tiny target detection |
| url | https://www.frontiersin.org/articles/10.3389/fphy.2024.1472584/full |
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