MST: Multiscale Flow-Based Student–Teacher Network for Unsupervised Anomaly Detection

Student–teacher networks have shown promise in unsupervised anomaly detection; however, issues such as semantic confusion and abnormal deformations still restrict the detection accuracy. To address these issues, we propose a novel student–teacher network named MST by integrating the multistage pixel-reserving bridge (MPRB) and the spatial compression autoencoder (SCA) to the MMR network. The MPRB enhances inter-level information interaction and local feature extraction, improving the anomaly localization and reducing the false detection area. The SCA bolsters global feature extraction, making the detection boundaries of larger defects clearer. By testing our network across various datasets, our method achieves state-of-the-art (SOTA) performance on AeBAD-S, AeBAD-V, and MPDD datasets, with image-level AUROC scores of 87.5%, 78.5%, and 96.5%, respectively. Furthermore, our method also exhibits competitive performance on the widely utilized MVTec AD dataset.