Anomaly Detection Model Based Visual Inspection Method for PCB Board Manufacturing Process

被引：0

作者：

Lee, Sang-Jeong ^{[1
]}

Seo, Sung-Bal ^{[2
]}

Bae, You-Suk ^{[2
]}

机构：

[1] Vision AI Business Team, LG CNS, Korea, Republic of

[2] Dept. of Computer Engineering, Tech University of Korea, Korea, Republic of

来源：

Transactions of the Korean Institute of Electrical Engineers | 2024年 / 73卷 / 11期

关键词：

Contrastive Learning - Deep learning - Smart manufacturing;

D O I：

10.5370/KIEE.2024.73.11.2024

中图分类号：

学科分类号：

摘要：

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. Copyright © The Korean Institute of Electrical Engineers.

引用

页码：2024 / 2029

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