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4 January 2023 Relational-based transfer learning for automatic optical inspection based on domain discrepancy
Erik I. Valle Salgado, Haoxin Yan, Yue Hong, Peiyuan Zhu, Shidong Zhu, Chengwei Liao, Yanxiang Wen, Xiu Li, Xiang Qian, Xiaohao Wang, Xinghui Li
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Proceedings Volume 12317, Optoelectronic Imaging and Multimedia Technology IX; 1231715 (2023) https://doi.org/10.1117/12.2644087
Event: SPIE/COS Photonics Asia, 2022, Online Only
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Abstract
Transfer learning is a promising method for AOI applications since it can significantly shorten sample collection time and improve efficiency in today’s smart manufacturing. However, related research enhanced the network models by applying TL without considering the domain similarity among datasets, the data long-tailedness of a source dataset, and mainly used linear transformations to mitigate the lack of samples. This research applies relational-based TL via domain similarity to improve the overall performance and data augmentation in both target and source domains to enrich the data quality and reduce the imbalance. Given a group of source datasets from similar industrial processes, we define which group is the most related to the target through the domain discrepancy score and the number of samples each has. Then, we transfer the chosen pre-trained backbone weights to train and fine-tune the target network. Our research suggests increases in the F1 score and the PR curve up to 20% compared with TL using benchmark datasets.
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Erik I. Valle Salgado, Haoxin Yan, Yue Hong, Peiyuan Zhu, Shidong Zhu, Chengwei Liao, Yanxiang Wen, Xiu Li, Xiang Qian, Xiaohao Wang, and Xinghui Li "Relational-based transfer learning for automatic optical inspection based on domain discrepancy", Proc. SPIE 12317, Optoelectronic Imaging and Multimedia Technology IX, 1231715 (4 January 2023); https://doi.org/10.1117/12.2644087
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KEYWORDS
Optical inspection
Defect detection
Manufacturing
Convolutional neural networks
Machine learning
Machine vision
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