MemSeg: A semi-supervised method for image surface defect detection using differences and commonalities
This addresses the problem of real-time defect detection in industrial manufacturing, offering an incremental improvement with faster inference speed.
The paper tackles surface defect detection on industrial products by proposing MemSeg, a semi-supervised memory-based segmentation network that uses simulated abnormal samples and memory pools to learn differences and commonalities, achieving state-of-the-art AUC scores of 99.56% (image-level) and 98.84% (pixel-level) on MVTec AD datasets.
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.