关键词: 无监督深度学习, 逻辑异常检测, 反向蒸馏, 多层次全局语义, 马氏距离

Abstract: Abstract: In modern assembly lines, industrial anomaly detection was challenged by dual tasks: ensuring the structural integrity of single-component surfaces while verifying logical assembly relationships among multiple components. Logical anomalies were defined as global defects violating product assembly constraints. Existing unsupervised anomaly detection methods were observed to lack an optimal balance between accuracy and speed for logical anomaly detection. To address this, LogicRD was proposed to enhance logical anomaly detection capability while preserving the local anomaly detection performance and low latency of reverse distillation (RD). First, a Global Semantic Enhancement (GSE) module was designed to transform multi-scale features from the teacher network into multi-level global semantic representations containing spatial-logical relationships through cross-hierarchical feature fusion. Second, a Feature Compensation Pathway (FCP) was introduced across network layers to alleviate fine-grained information loss in global semantics, thereby improving reconstruction accuracy of the student network. A Normal Information Inhibition (NII) gate mechanism was simultaneously implemented to eliminate potential anomaly leakage risks. Finally, considering that logical anomalies could not be effectively expressed through anomaly maps, Mahalanobis distance was employed to directly measure feature deviations. Logical anomalies were evaluated jointly through this metric and anomaly maps, establishing a dual-branch evaluation strategy for comprehensive industrial anomaly assessment. On the MVTec LOCO benchmark, LogicRD achieved 83.8% image-level AUROC with 55 FPS inference speed, showing 7 percentage-point improvement over baseline RD. Comparable performance was attained with DSKD (a dual-branch logical anomaly detection model based on RD), while achieving 1.7× faster inference speed.

Key words: unsupervised deep learning, logic anomaly detection, reverse distillation, multi-layered global semantics, mahalanobis distance