Few-Shot PCB Surface Defect Detection Based on Feature Enhancement and Multi-Scale Fusion
In printed circuit board (PCB) defect detection, it is difficult to collect defect samples, and the detection effect is poor due to the lack of data. On the basis of the few-shot learning method, a few-shot PCB defect detection model is proposed. This model introduces feature enhancement module and...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9979794/ |
| _version_ | 1797980083982434304 |
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| author | Haodong Wang Jun Xie Xinying Xu Zihao Zheng |
| author_facet | Haodong Wang Jun Xie Xinying Xu Zihao Zheng |
| author_sort | Haodong Wang |
| collection | DOAJ |
| description | In printed circuit board (PCB) defect detection, it is difficult to collect defect samples, and the detection effect is poor due to the lack of data. On the basis of the few-shot learning method, a few-shot PCB defect detection model is proposed. This model introduces feature enhancement module and multi-scale fusion module. The feature enhancement module based on the improved convolution block attention module (CBAM) can highlight the key areas of the received feature maps and suppress the interference of useless information. Aiming at the small size of PCB defects, a multi-scale feature fusion strategy is proposed. It can extract multi-scale feature maps of PCB and fuse them into a high-quality feature map containing different scale information, which can improve the detection precision of the model for small object defects. A large number of experiments on PCB dataset show that our few-shot PCB defect detection model outperforms state-of-the-art methods under different shot settings (<inline-formula> <tex-math notation="LaTeX">$\text{k}=1$ </tex-math></inline-formula>,2,3,5,10,30). Notably, the proposed model can take into account both detection efficiency and precision, which means it has high practical application value. |
| first_indexed | 2024-04-11T05:49:46Z |
| format | Article |
| id | doaj.art-5456fed2772c4886ba7d72913ad12d15 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T05:49:46Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-5456fed2772c4886ba7d72913ad12d152022-12-22T04:42:06ZengIEEEIEEE Access2169-35362022-01-011012991112992410.1109/ACCESS.2022.32283929979794Few-Shot PCB Surface Defect Detection Based on Feature Enhancement and Multi-Scale FusionHaodong Wang0https://orcid.org/0000-0001-6099-0040Jun Xie1https://orcid.org/0000-0003-0955-9970Xinying Xu2https://orcid.org/0000-0001-5968-5989Zihao Zheng3https://orcid.org/0000-0001-6792-9066College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan, ChinaCollege of Information and Computer, Taiyuan University of Technology, Jinzhong, ChinaCollege of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan, ChinaCollege of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan, ChinaIn printed circuit board (PCB) defect detection, it is difficult to collect defect samples, and the detection effect is poor due to the lack of data. On the basis of the few-shot learning method, a few-shot PCB defect detection model is proposed. This model introduces feature enhancement module and multi-scale fusion module. The feature enhancement module based on the improved convolution block attention module (CBAM) can highlight the key areas of the received feature maps and suppress the interference of useless information. Aiming at the small size of PCB defects, a multi-scale feature fusion strategy is proposed. It can extract multi-scale feature maps of PCB and fuse them into a high-quality feature map containing different scale information, which can improve the detection precision of the model for small object defects. A large number of experiments on PCB dataset show that our few-shot PCB defect detection model outperforms state-of-the-art methods under different shot settings (<inline-formula> <tex-math notation="LaTeX">$\text{k}=1$ </tex-math></inline-formula>,2,3,5,10,30). Notably, the proposed model can take into account both detection efficiency and precision, which means it has high practical application value.https://ieeexplore.ieee.org/document/9979794/PCB defect detectionfew-shot learningfeature enhancementmulti-scale fusion |
| spellingShingle | Haodong Wang Jun Xie Xinying Xu Zihao Zheng Few-Shot PCB Surface Defect Detection Based on Feature Enhancement and Multi-Scale Fusion IEEE Access PCB defect detection few-shot learning feature enhancement multi-scale fusion |
| title | Few-Shot PCB Surface Defect Detection Based on Feature Enhancement and Multi-Scale Fusion |
| title_full | Few-Shot PCB Surface Defect Detection Based on Feature Enhancement and Multi-Scale Fusion |
| title_fullStr | Few-Shot PCB Surface Defect Detection Based on Feature Enhancement and Multi-Scale Fusion |
| title_full_unstemmed | Few-Shot PCB Surface Defect Detection Based on Feature Enhancement and Multi-Scale Fusion |
| title_short | Few-Shot PCB Surface Defect Detection Based on Feature Enhancement and Multi-Scale Fusion |
| title_sort | few shot pcb surface defect detection based on feature enhancement and multi scale fusion |
| topic | PCB defect detection few-shot learning feature enhancement multi-scale fusion |
| url | https://ieeexplore.ieee.org/document/9979794/ |
| work_keys_str_mv | AT haodongwang fewshotpcbsurfacedefectdetectionbasedonfeatureenhancementandmultiscalefusion AT junxie fewshotpcbsurfacedefectdetectionbasedonfeatureenhancementandmultiscalefusion AT xinyingxu fewshotpcbsurfacedefectdetectionbasedonfeatureenhancementandmultiscalefusion AT zihaozheng fewshotpcbsurfacedefectdetectionbasedonfeatureenhancementandmultiscalefusion |