RD-RE: Reverse Distillation with Feature Reconstruction Enhancement for Industrial Anomaly Detection

Abstract

Industrial anomaly detection methods based on reverse distillation (RD) have shown significant potential. However, existing RD approaches struggle to achieve an effective balance between constraining the feature consistency of the teacher–student networks and maintaining differentiated representation capability, which is crucial for precise anomaly detection. To address this challenge, we propose Reverse Distillation with Feature Reconstruction Enhancement (RD-RE) for Industrial Anomaly Detection. Firstly, we design a cross-stage feature fusion student network to integrate spatial detail information from the encoder with rich semantic information from the decoder. Secondly, we introduce a Locally Aware Dynamic Attention (LDA) module to enhance local detail feature response, thereby improving the model’s robustness in capturing anomalous regions. Finally, a Context-Aware Adaptive Multi-Scale Feature Fusion (CFFMS-FF) module is designed to constrain the consistency of local feature reconstruction. Experiments on the MVTec AD benchmark dataset demonstrate the effectiveness of RD-RE, achieving competitive results of 99.0%, 95.8%, 78.3%, and 99.7% on pixel-level AUROC, PRO, and AP and image-level AUROC metrics, and outperforming existing RD-based approaches. These results conclude that the integration of cross-stage fusion and local attention effectively mitigates the representation-consistency trade-off, providing a more robust solution for industrial anomaly localization.