Abstract
This paper designs a deep model to detect PCB defects from an input pair of a detect-free template and a defective tested image. A novel group pyramid pooling module is proposed to efficiently extract features in various resolutions to predict defects in different scales. To train the deep model, a dataset including 6 common types of PCB defects is established, namely DeepPCB, which contains 1,500 image pairs with annotations. Besides, a semi-supervised learning manner is examined to effectively utilize the unlabelled images for training the PCB defect detector. Experiment results validate the effectiveness and efficiency of the proposed model by achieving 98.6% mAP @ 62 FPS on DeepPCB dataset. DeepPCB is now available at: https://github.com/tangsanli5201/DeepPCB.
Original language | English |
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Title of host publication | ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning |
Publisher | ESANN (i6doc.com) |
Pages | 527-532 |
Number of pages | 6 |
ISBN (Electronic) | 9782875870742 |
Publication status | Published - 2020 |
Event | 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2020 - Virtual, Online, Belgium Duration: 2 Oct 2020 → 4 Oct 2020 |
Publication series
Name | ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning |
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Conference
Conference | 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2020 |
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Country/Territory | Belgium |
City | Virtual, Online |
Period | 2/10/20 → 4/10/20 |
Bibliographical note
Publisher Copyright:© ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.