A comprehensive review of research on surface defect detection of PCBs based on machine vision
Printed circuit board (PCB) is a crucial component of advanced electronic devices, and its quality control cannot be overlooked. This paper presents a comprehensive review of machine vision-based surface defect detection methods for PCBs, addressing the transition from traditional image processing t...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
|
| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S259012302502506X |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850065045621309440 |
|---|---|
| author | Zihan He Yudong Lian Yulei Wang Zhiwei Lu |
| author_facet | Zihan He Yudong Lian Yulei Wang Zhiwei Lu |
| author_sort | Zihan He |
| collection | DOAJ |
| description | Printed circuit board (PCB) is a crucial component of advanced electronic devices, and its quality control cannot be overlooked. This paper presents a comprehensive review of machine vision-based surface defect detection methods for PCBs, addressing the transition from traditional image processing to advanced deep learning techniques. With the increasing complexity of PCB designs and the demand for high-precision manufacturing, automated defect detection has become critical for quality control. The study introduces nine public datasets for PCB surface inspection and fourteen common types of PCB surface defects, and provides an overview of commonly used performance evaluation metrics in the field of PCB defect detection. This paper systematically analyzes three categories of detection approaches: image processing-based methods, machine learning-based classifiers, and deep learning architectures. Furthermore, the review provides a comparative evaluation of representative works for each method, revealing that deep learning-based methods achieve state-of-the-art performance, while image processing methods and traditional machine learning approaches remain valuable in resource-constrained scenarios. We believe that the Transformer architecture has transformative potential in handling global defect patterns, while emphasizing the need for lightweight model architectures through techniques like knowledge distillation. This paper also mentions hybrid methods and ensemble approaches, which leverage the advantages of different detection techniques to achieve defect detection, improve detection accuracy, and reduce the probabilities of false positives and false negatives. Key challenges such as class imbalance, computational complexity, and detection of micro-defects are discussed, along with future directions including self-supervised learning, multi-modal learning, 3D defect detection, and edge-cloud collaborative systems. |
| format | Article |
| id | doaj-art-5e864c73a5f947029261b1e1bb5fcdaf |
| institution | DOAJ |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-5e864c73a5f947029261b1e1bb5fcdaf2025-08-20T02:49:06ZengElsevierResults in Engineering2590-12302025-09-012710643710.1016/j.rineng.2025.106437A comprehensive review of research on surface defect detection of PCBs based on machine visionZihan He0Yudong Lian1Yulei Wang2Zhiwei Lu3Center for Advanced Laser Technology, Hebei University of Technology, Tianjin 300401, China; Hebei Key Laboratory of Advanced Laser Technology and Equipment, Tianjin 300401, ChinaCenter for Advanced Laser Technology, Hebei University of Technology, Tianjin 300401, China; Hebei Key Laboratory of Advanced Laser Technology and Equipment, Tianjin 300401, China; Corresponding author at: Center for Advanced Laser Technology, Hebei University of Technology, Tianjin 300401, China.Center for Advanced Laser Technology, Hebei University of Technology, Tianjin 300401, China; Hebei Key Laboratory of Advanced Laser Technology and Equipment, Tianjin 300401, China; Corresponding author at: Center for Advanced Laser Technology, Hebei University of Technology, Tianjin 300401, China.Center for Advanced Laser Technology, Hebei University of Technology, Tianjin 300401, China; Hebei Key Laboratory of Advanced Laser Technology and Equipment, Tianjin 300401, ChinaPrinted circuit board (PCB) is a crucial component of advanced electronic devices, and its quality control cannot be overlooked. This paper presents a comprehensive review of machine vision-based surface defect detection methods for PCBs, addressing the transition from traditional image processing to advanced deep learning techniques. With the increasing complexity of PCB designs and the demand for high-precision manufacturing, automated defect detection has become critical for quality control. The study introduces nine public datasets for PCB surface inspection and fourteen common types of PCB surface defects, and provides an overview of commonly used performance evaluation metrics in the field of PCB defect detection. This paper systematically analyzes three categories of detection approaches: image processing-based methods, machine learning-based classifiers, and deep learning architectures. Furthermore, the review provides a comparative evaluation of representative works for each method, revealing that deep learning-based methods achieve state-of-the-art performance, while image processing methods and traditional machine learning approaches remain valuable in resource-constrained scenarios. We believe that the Transformer architecture has transformative potential in handling global defect patterns, while emphasizing the need for lightweight model architectures through techniques like knowledge distillation. This paper also mentions hybrid methods and ensemble approaches, which leverage the advantages of different detection techniques to achieve defect detection, improve detection accuracy, and reduce the probabilities of false positives and false negatives. Key challenges such as class imbalance, computational complexity, and detection of micro-defects are discussed, along with future directions including self-supervised learning, multi-modal learning, 3D defect detection, and edge-cloud collaborative systems.http://www.sciencedirect.com/science/article/pii/S259012302502506XPrinted Circuit board (PCB) defect detectionQuality controlMachine learningDeep learningsmart manufacturing |
| spellingShingle | Zihan He Yudong Lian Yulei Wang Zhiwei Lu A comprehensive review of research on surface defect detection of PCBs based on machine vision Results in Engineering Printed Circuit board (PCB) defect detection Quality control Machine learning Deep learning smart manufacturing |
| title | A comprehensive review of research on surface defect detection of PCBs based on machine vision |
| title_full | A comprehensive review of research on surface defect detection of PCBs based on machine vision |
| title_fullStr | A comprehensive review of research on surface defect detection of PCBs based on machine vision |
| title_full_unstemmed | A comprehensive review of research on surface defect detection of PCBs based on machine vision |
| title_short | A comprehensive review of research on surface defect detection of PCBs based on machine vision |
| title_sort | comprehensive review of research on surface defect detection of pcbs based on machine vision |
| topic | Printed Circuit board (PCB) defect detection Quality control Machine learning Deep learning smart manufacturing |
| url | http://www.sciencedirect.com/science/article/pii/S259012302502506X |
| work_keys_str_mv | AT zihanhe acomprehensivereviewofresearchonsurfacedefectdetectionofpcbsbasedonmachinevision AT yudonglian acomprehensivereviewofresearchonsurfacedefectdetectionofpcbsbasedonmachinevision AT yuleiwang acomprehensivereviewofresearchonsurfacedefectdetectionofpcbsbasedonmachinevision AT zhiweilu acomprehensivereviewofresearchonsurfacedefectdetectionofpcbsbasedonmachinevision AT zihanhe comprehensivereviewofresearchonsurfacedefectdetectionofpcbsbasedonmachinevision AT yudonglian comprehensivereviewofresearchonsurfacedefectdetectionofpcbsbasedonmachinevision AT yuleiwang comprehensivereviewofresearchonsurfacedefectdetectionofpcbsbasedonmachinevision AT zhiweilu comprehensivereviewofresearchonsurfacedefectdetectionofpcbsbasedonmachinevision |