Knowledge-prior memory discrimination based generative adversarial network for industrial anomaly detection and...

Industrial anomaly detection and localization have become key procedures in modern manufacturing for product quality assurance. However, it is still challenging for the emerging deep learning-based unsupervised anomaly detection approaches to simultaneously realize high-precision anomaly detection and localization, and generate high-quality anomaly masks end-to-end. Meanwhile, the limited number of normal samples and random nature of anomalies leads to insufficient discriminative information available for model training. To this end, a novel knowledge-prior memory discrimination based generative adversarial network (KMDGAN) is demonstrated for high-performance industrial anomaly detection and localization with merely few anomaly samples utilized. KMDGAN leverages both normal and anomaly features to learn feature-level spatial relationships and generate accurate anomaly masks end-to-end. Firstly, based on the idea of knowledge-prior discrimination, a memory feature matching module is elaborately designed to construct a memory knowledge base of normal product features, and considers the positive and negative difference information from the spatial location features, promoting the detection and localization of abnormal regions. Secondly, a cross-scale feature fusion module is developed to solve the multi-scale feature redundancy problem via attention optimization, while an anomaly map modulation technique enhances the integration of global context and local details. Finally, an edge-context hybrid module is designed to address the limitation of single information and enhance the perceptual comprehension ability by fully extracting the edge and overall information of anomaly sample features. Experimental results on the MVTec AD and VisA datasets demonstrate that our KMDGAN model achieves a salient anomaly detection and localization performance, outperforming the existing advanced counterparts.