Incorporating long-tail data in complex backgrounds for visual surface defect detection in PCBs
Abstract High-quality printed circuit boards (PCBs) are essential components in modern electronic circuits. Nevertheless, most of the existing methods for PCB surface defect detection neglect the fact that PCB surface defects in complex backgrounds are prone to long-tailed data distributions, which...
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| Format: | Article |
| Language: | English |
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Springer
2024-07-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-024-01554-5 |
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| author | Liying Zhu Sen Wang Mingfang Chen Aiping Shen Xuangang Li |
| author_facet | Liying Zhu Sen Wang Mingfang Chen Aiping Shen Xuangang Li |
| author_sort | Liying Zhu |
| collection | DOAJ |
| description | Abstract High-quality printed circuit boards (PCBs) are essential components in modern electronic circuits. Nevertheless, most of the existing methods for PCB surface defect detection neglect the fact that PCB surface defects in complex backgrounds are prone to long-tailed data distributions, which in turn affects the effectiveness of defect detection. Additionally, most of the existing methods ignore the intra-scale features of defects and do not utilize auxiliary supervision strategies to improve the detection performance of the network. To tackle these issues, we propose a lightweight long-tailed data mining network (LLM-Net) for identifying PCB surface defects. Firstly, the proposed Efficient Feature Fusion Network (EFFNet) is applied to embed intra-scale feature associations and multi-scale features of defects into LLM-Net. Next, an auxiliary supervision method with a soft label assignment strategy is designed to help LLM-Net learn more accurate defect features. Finally, the issue of inadequate tail data detection is addressed by employing the devised Binary Cross-Entropy Loss Rank Mining method (BCE-LRM) to identify challenging samples. The performance of LLM-Net was evaluated on a homemade dataset of PCB surface soldering defects, and the results show that LLM-Net achieves the best accuracy of mAP@0.5 for the evaluation metric of the COCO dataset, and it has a real-time inference speed of 188 frames per second (FPS). |
| format | Article |
| id | doaj-art-75e9a19c662c4c5f90db5d10979da754 |
| institution | OA Journals |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-75e9a19c662c4c5f90db5d10979da7542025-08-20T02:17:49ZengSpringerComplex & Intelligent Systems2199-45362198-60532024-07-011067591760410.1007/s40747-024-01554-5Incorporating long-tail data in complex backgrounds for visual surface defect detection in PCBsLiying Zhu0Sen Wang1Mingfang Chen2Aiping Shen3Xuangang Li4Faculty of Mechanical and Electrical Engineering, Kunming University of Science and TechnologyFaculty of Mechanical and Electrical Engineering, Kunming University of Science and TechnologyFaculty of Mechanical and Electrical Engineering, Kunming University of Science and TechnologyFaculty of Mechanical and Electrical Engineering, Kunming University of Science and TechnologyFaculty of Mechanical and Electrical Engineering, Kunming University of Science and TechnologyAbstract High-quality printed circuit boards (PCBs) are essential components in modern electronic circuits. Nevertheless, most of the existing methods for PCB surface defect detection neglect the fact that PCB surface defects in complex backgrounds are prone to long-tailed data distributions, which in turn affects the effectiveness of defect detection. Additionally, most of the existing methods ignore the intra-scale features of defects and do not utilize auxiliary supervision strategies to improve the detection performance of the network. To tackle these issues, we propose a lightweight long-tailed data mining network (LLM-Net) for identifying PCB surface defects. Firstly, the proposed Efficient Feature Fusion Network (EFFNet) is applied to embed intra-scale feature associations and multi-scale features of defects into LLM-Net. Next, an auxiliary supervision method with a soft label assignment strategy is designed to help LLM-Net learn more accurate defect features. Finally, the issue of inadequate tail data detection is addressed by employing the devised Binary Cross-Entropy Loss Rank Mining method (BCE-LRM) to identify challenging samples. The performance of LLM-Net was evaluated on a homemade dataset of PCB surface soldering defects, and the results show that LLM-Net achieves the best accuracy of mAP@0.5 for the evaluation metric of the COCO dataset, and it has a real-time inference speed of 188 frames per second (FPS).https://doi.org/10.1007/s40747-024-01554-5PCB defect detectionObject detectionEFFNetAuxiliary supervisionBCE-LRM |
| spellingShingle | Liying Zhu Sen Wang Mingfang Chen Aiping Shen Xuangang Li Incorporating long-tail data in complex backgrounds for visual surface defect detection in PCBs Complex & Intelligent Systems PCB defect detection Object detection EFFNet Auxiliary supervision BCE-LRM |
| title | Incorporating long-tail data in complex backgrounds for visual surface defect detection in PCBs |
| title_full | Incorporating long-tail data in complex backgrounds for visual surface defect detection in PCBs |
| title_fullStr | Incorporating long-tail data in complex backgrounds for visual surface defect detection in PCBs |
| title_full_unstemmed | Incorporating long-tail data in complex backgrounds for visual surface defect detection in PCBs |
| title_short | Incorporating long-tail data in complex backgrounds for visual surface defect detection in PCBs |
| title_sort | incorporating long tail data in complex backgrounds for visual surface defect detection in pcbs |
| topic | PCB defect detection Object detection EFFNet Auxiliary supervision BCE-LRM |
| url | https://doi.org/10.1007/s40747-024-01554-5 |
| work_keys_str_mv | AT liyingzhu incorporatinglongtaildataincomplexbackgroundsforvisualsurfacedefectdetectioninpcbs AT senwang incorporatinglongtaildataincomplexbackgroundsforvisualsurfacedefectdetectioninpcbs AT mingfangchen incorporatinglongtaildataincomplexbackgroundsforvisualsurfacedefectdetectioninpcbs AT aipingshen incorporatinglongtaildataincomplexbackgroundsforvisualsurfacedefectdetectioninpcbs AT xuangangli incorporatinglongtaildataincomplexbackgroundsforvisualsurfacedefectdetectioninpcbs |