SSDC-Net: An Effective Classification Method of Steel Surface Defects Based on Salient Local Features

To effectively classify steel surface defects, it is crucial to improve the feature representation. In this paper, we propose the Steel Surface Defect Classification Network SSDC-Net to optimize steel surface defect classification task. This framework includes the Channel Information Complementary Matrix CICM module and Multi-Scale Contrastive Loss MCL method. Specifically, the CICM module utilizes interactive information between channels of the feature map to enhance local feature representation. To enhance global feature representation, we further introduce a multi-scale contrastive loss, which not only maximizes differences between features but also reduces the gap between different views of the same sample. Extensive experiments conducted on the BG-DET dataset and FSC-20 dataset demonstrate the effectiveness of each proposed module. Compared with other methods, our approach has achieved state-of-the-art performance. Additionally, our method is plug-and-play and can be seamlessly integrated into other backbone networks.