  Under the semi-supervised framework, we propose an end-to-end memory-based
segmentation network (MemSeg) to detect surface defects on industrial products.
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
learning of the network. 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 manner. Through
experimental validation, MemSeg achieves the 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. In addition, MemSeg also has a
significant advantage in inference speed benefiting from the end-to-end and
straightforward network structure, which better meets the real-time requirement
in industrial scenarios.
