MemSeg: A semi-supervised method for image surface defect detection using differences and commonalities

High-accuracy and real-time semi-supervised image surface defect detection is extensively needed in industrial scenarios. However, existing methods do not provide a good balance between accuracy and speed of defect detection, so this paper proposes an end-to-end memory-based segmentation network (MemSeg) to better accomplish this task. Considering the small intra-class variance of products in the same production line, from the perspective of differences and commonalities, MemSeg introduces artificially simulated abnormal samples and memory samples to assist the model learning. In the training phase, MemSeg explicitly learns the potential differences between normal and simulated abnormal images to obtain a robust classification hyperplane. At the same time, inspired by the mechanism of human memory, MemSeg uses a memory pool to store the general patterns of normal samples. By comparing the similarities and differences between input samples and memory samples in the memory pool to give effective guesses about abnormal regions; In the inference phase, MemSeg directly determines the abnormal regions of the input image in an end-to-end approach. Simple but high-performance, MemSeg achieves state-of-the-art (SOTA) performance on MVTec AD datasets with AUC scores of 99.56% and 98.84% at the image level and pixel level, respectively, while also meeting the real-time requirements in industrial scenarios.