UAMS: An Unsupervised Anomaly Detection Method Integrating MSAA and SSPCAB

Anomaly detection methods play a crucial role in automated quality control within modern manufacturing systems. In this context, unsupervised methods are increasingly favored due to their independence from large-scale labeled datasets. However, existing methods present limited multi-scale feature extraction ability and may fail to effectively capture subtle anomalies. To address these challenges, we propose UAMS, a pyramid-structured normalization flow framework that leverages the symmetry in feature recombination to harmonize multi-scale interactions. The proposed framework integrates a Multi-Scale Attention Aggregation (MSAA) module for cross-scale dynamic fusion, as well as a Self-Supervised Predictive Convolutional Attention Block (SSPCAB) for spatial channel attention and masked prediction learning. Experiments on the MVTecAD dataset show that UAMS largely outperforms state-of-the-art unsupervised methods, in terms of detection and localization accuracy, while maintaining high inference efficiency. For example, when comparing UAMS against the baseline model on the carpet category, the AUROC is improved from 90.8% to 94.5%, and AUPRO is improved from 91.0% to 92.9%. These findings validate the potential of the proposed method for use in real industrial inspection scenarios.