MuDeNet: A multi-patch descriptor network for anomaly modeling

Abstract

Visual anomaly detection is a crucial task in industrial manufacturing, enabling early defect identification and minimizing production bottlenecks. Existing methods often struggle to effectively detect both structural anomalies, which appear as unexpected local patterns, and logical anomalies, which arise from violations of global contextual constraints. To address this challenge, we propose MuDeNet, an unsupervised Multi-patch Descriptor Network that performs multi-scale fusion of local structural features and global contextual information for comprehensive anomaly modeling. MuDeNet employs a lightweight teacher-student framework that jointly extracts and fuses local and global patch descriptors across multiple receptive fields within a single forward pass. Knowledge is first distilled from a pre-trained CNN to efficiently obtain semantic representations, which are then processed by two complementary modules: the structural module, targeting fine-grained defects at small receptive fields, and the logical module, modeling long-range contextual dependencies. Their outputs are fused at the decision level, yielding a unified anomaly score that integrates local and global evidence. Extensive experiments on three state-of-the-art datasets position MuDeNet as an efficient and scalable solution for real-time industrial anomaly detection and segmentation, consistently outperforming existing approaches.