CMTSiMBA-light: Industrial Defect Classification and Detection Based on Deep Learning

Abstract

Surface quality inspection of industrial products is a crucial step in ensuring production line efficiency and product quality. In response to the problem of insufficient fusion of local and global features in image classification algorithms in complex industrial scenarios and the difficulty of a single architecture to balance texture details and long-range dependencies. This paper focuses on deep learning-based industrial defect classification and detection, and proposes and optimizes the relevant algorithm CMTSiMBA-light. By incorporating the SENet convolution module into the CMT Stem and replacing traditional convolution with the PConv convolution module in the SiMBA Stem, the model's accuracy and efficiency have been significantly improved. Experiments show that the model improves accuracy by 0.7% and 1.4% respectively on the NEU-CLS and FSC-20 datasets compared to CMT, reduces parameters by 10.8%, lowers FLOPs by 5%, and increases throughput by 5.2%