Hongmin Hu

Industrial image anomaly detection requires the simultaneous identification of local structural and global logical anomalies. Existing methods specialize in single-type anomalies due to divergent feature requirements: structural anomalies demand fine-grained local features, while logical anomalies need semantic features. Consequently, designing a unified network architecture that effectively captures both features without task conflicts remains a key challenge. To address this problem, we propose a Dual-Stream Memory Bank Anomaly Detection (DSMBAD) framework, which enables the collaborative detection of both structural and logical anomalies from complementary perspectives. The framework consists of two memory banks: one stores patch features for detecting structural anomalies through local feature discrepancies, while the other uses segmentation maps to model component relationships for logical anomaly identification. Additionally, a feature distillation mechanism aligns features from different backbone networks to enhance global semantic information. We also introduce a shape-based anomaly scoring method that quantifies differences in component relationships using spatial–morphological features. Experimental results on the MVTec LOCO AD dataset show that our method achieves 91.0% I-AUROC (logical) and 90.8% (structural), significantly outperforming single-type models. Ablation studies confirm the dual-stream design and module effectiveness, offering a novel unified solution.

In modern industrial production, unsupervised anomaly detection methods have gained significant attention due to their ability to address the challenge posed by the scarcity of labeled anomaly samples. Among them, unsupervised anomaly detection methods based on reverse distillation (RD) have become a mainstream choice, which has attracted extensive research due to their excellent anomaly detection performance. However, there is a problem of “feature leakage” in the RD model, which may lead to non-anomalous regions being incorrectly identified as defects. To solve this problem, we propose a Normal Feature-Enhanced Reverse teacher–student Distillation (NFERD) method. Specifically, we designed and incorporated a normal feature bank (NFB) module into the basic RD network. This module stores normal features extracted by the teacher model, assisting the student model in learning normal features more efficiently, thereby addressing the problem of “feature leakage”. In addition, to effectively fuse the feature maps extracted by the student model with the feature maps in NFBs, we designed a Hybrid Attention Fusion Module (HAFM), which ensures the preservation of key information during the feature fusion process by the parallel processing of spatial and channel attention mechanisms. Through experimental verification on two publicly available datasets, i.e., MVTec and KSDD, our method outperformed the existing mainstream methods in both image-level and pixel-level anomaly detection. Specifically, we achieved an average I-AUROC score of 99.32% on MVTec and a 98.75% P-AUROC on the KSDD, showing clearer segmentation results, especially in complex scenarios. Furthermore, our method surpassed the second-best method by over 1.4% PRO on MVTec, demonstrating its effectiveness.