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

Understand

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.