MemFlowNet: A Network for Detecting Subtle Surface Anomalies with Memory Bank and Normalizing Flow

Detection of subtle surface anomalies in the presence of strong noise is a challenging vision task. This paper presents a new neural network called MemFlowNet for detecting subtle surface anomalies by combining the advantages of the memory bank and normalizing flow. The proposed method consists of two stages. The first stage achieves pixel-level segmentation of anomalies using noise-insensitive average features in the memory bank and Nearest Neighbor search strategy, and the second stage performs image-level detection using normalizing flows and multi-scale score fusion. A new dataset called INSCup has been developed to assist this research by acquiring inner surface images of stainless steel insulated cups with ultra-wide lenses. The performance of MemFlowNet has been validated on INSCup dataset by surpassing other mainstream methods. In addition, MemFlowNet achieves the best performance with an image-level AUROC of 99.57% in anomaly detection of MVTec-AD benchmark. It shows a great potential to apply MemFlowNet to automated visual inspection of surface anomalies.