earticle

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.