Earticle

초록

영어

The imbalance of datasets is a significant challenge in training deep neural networks. Especially in manufacturing, there is only one form of ‘normal’, while defects are endless. This disproportion in sample distribution makes models prone to overfitting, resulting in degraded performance. To mitigate this problem, we propose C4, a Color-Channel Concatenation with Contrastive Loss, a defect detection framework based on Siamese Networks. We performed a case study on industrial automation technologies, especially in sealant defect classification. C4 achieves an F1- score of 94.54% and an accuracy of 94.21%, demonstrating its effectiveness in handling class imbalance.

목차

Abstract
I. INTRODUCTION
II. METHODOLOGY
III. EXPERIMENTS AND RESULTS
A. Experiment Settings
B. Experiment Results
IV. CONCLUSION AND FUTURE WORK
ACKNOWLEDGMENT
REFERENCES

저자

• Joonha Park [ School of Computer Science and Information Engineering The Catholic University of Korea Bucheon, South Korea ]
• Siyoung Kim [ Department of Computer Engineering The Catholic University of Korea Bucheon, South Korea ]
• Sihyung Kim [ Department of Computer Engineering The Catholic University of Korea Bucheon, South Korea ]
• Jaehyun Cha [ Department of Computer Engineering The Catholic University of Korea Bucheon, South Korea ]
• Wonsuk Kim [ Safe AI Seoul, South Korea ]
• Yoojoong Kim [ School of Computer Science and Information Engineering The Catholic University of Korea Bucheon, South Korea ] Corresponding Author

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