A deep learning approach for automated PCB defect detection: A comprehensive review
The ever-expanding market for electronic devices has significantly heightened the demand for high-quality printed circuit boards (PCBs). Even minor defects in PCBs can pose substantial safety risks for end-users. This article provides a comprehensive review on deep learning-based approaches for PC...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Malque Publishing
2024
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| Online Access: | http://eprints.utem.edu.my/id/eprint/28825/2/0112231122024194837.pdf http://eprints.utem.edu.my/id/eprint/28825/ https://malque.pub/ojs/index.php/mr/article/view/5118/2443 https://doi.org/10.31893/multirev.2025011 |
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| Summary: | The ever-expanding market for electronic devices has significantly heightened the demand for high-quality printed
circuit boards (PCBs). Even minor defects in PCBs can pose substantial safety risks for end-users. This article provides a
comprehensive review on deep learning-based approaches for PCB defect detection. Our exploration covers various critical aspects, including the classification of PCB defects, automated vision inspection (AVI) techniques, object detection methodologies, and the widespread adoption of deep learning models. Specifically, we focus on the state-of-the-art approach known as region-based Fully Convolutional Network with Feature Pyramid Networks (FPN-RFCN). Additionally, we discuss effective data augmentation techniques and commonly used evaluation metrics in this domain. This review provides valuable insights for researchers, practitioners, and industry professionals engaged in PCB quality assurance. |
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