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An Attention-Enhanced YOLO Neck for Tiny PCB Bubble Detection
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- 발행기관
- 국제인공지능학회(구 한국인터넷방송통신학회) 바로가기
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- 간행물
- International Journal of Internet, Broadcasting and Communication 바로가기
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- 통권
- Vol.17 No.4 (2025.11)바로가기
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- 페이지
- pp.429-440
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- 저자
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- 언어
- 영어(ENG)
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- URL
- https://www.earticle.net/Article/A486501
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
- 영어
- Bubble defects reduce the reliability of PCB coatings. Accurate, real-time detection of small bubbles is crucial. In this study, we present a fast and lightweight YOLOv8n-based detector. It improves the average precision of small bubbles by approximately 12 points. It also increases mAP50 from 0.637 to 0.697 in 49.1 milliseconds per image. This is a practical solution that supports quality control of micro-defects. NAMAttention reweights channel and spatial features. C2F fuses backbone and neck features. The P2 layer expands the receptive field for micro-bubbles. During training, size-aware loss emphasizes small bubbles. Defect-balanced sampling addresses class imbalance. Glare synthesis improves robustness to illumination variations. During inference, Soft-NMS reduces false positives caused by overlapping boxes. Our contribution lies in the organic integration of a learning strategy specialized for micro-defects and architectural improvements. At the same time, we maintain a lightweight structure and improve detection performance and latency. We quantitatively verified the utility and practical feasibility of each component through module-bymodule erasure experiments. We also performed throughput evaluations in field-deployment scenarios. This approach is effective for PCB quality control and suitable for detecting micro-defects in similar manufacturing processes.
목차
Abstract
1. Introduction
2. Related Work
3. Methodology
3.1 Backbone
3.2 Neck and Head
3.3 Inference
3.4 Training Loss and Sampling
4. Model Training and Validation
5. Experimental and Evaluation
5.1 Experimental Setup and Quantitative Results
5.2 Qualitative Analysis
6. Results
7. Discussion
8. Conclusion
Appendix 1
References
저자
간행물 정보
발행기관
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- 발행기관명
- 국제인공지능학회(구 한국인터넷방송통신학회) [The International Association for Artificial Intelligence]
- 설립연도
- 2000
- 분야
- 공학>전자/정보통신공학
- 소개
- 인터넷방송, 인터넷 TV , 방송 통신 네트워크 및 관련 분야에 대한 국내는 물론 국제적인 학술, 기술의 진흥발전에 공헌하고 지식 정보화 사회에 기여하고자 한다.
간행물
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- 간행물명
- International Journal of Internet, Broadcasting and Communication
- 간기
- 계간
- pISSN
- 2288-4920
- eISSN
- 2288-4939
- 수록기간
- 2009~2025
- 십진분류
- KDC 326 DDC 380
이 권호 내 다른 논문 / International Journal of Internet, Broadcasting and Communication Vol.17 No.4
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