KIEE - The Transactions of the Korean Institute of Electrical Engineers

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The Transactions of the Korean Institute of Electrical Engineers

The Transactions of the Korean Institute of Electrical Engineers

ISO Journal TitleTrans. Korean. Inst. Elect. Eng.

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Title	Anomaly Detection Model Based Visual Inspection Method for PCB Board Manufacturing Process
Authors	이상정(Sang-Jeong Lee) ; 서성발(Sung-Bal Seo) ; 배유석(You-Suk Bae)
DOI	https://doi.org/10.5370/KIEE.2024.73.11.2024
Page	pp.2024-2029
ISSN	1975-8359
Keywords	Deep learning; Anomaly detection; Feature extractor; Intelligent manufacturing; Vision inspection
Abstract	We developed a visual inspection method for PCB board using an anomaly detection model. To improve feature extraction performance, we developed and optimized the feature extractor by comparing three types of backbone models. Then we compared two anomaly detection models with developed feature extractor as a backbone for visual inspection. Finally, we found the optimized loss function named mean-shifted contrastive loss which showed the highest accuracy in our experiment.

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