APG-Net: Adaptive Prototype Guidance Network for Multi-Sensor Industrial Anomaly Detection

Since the general representations produced by pre-trained feature extractors are often insensitive to intra-class variations, existing anomaly detection methods that store them directly in memory banks are constrained in performance. In addition, the distribution discrepancy among different modalities may cause cross-modal interference, further weakening the ability to discriminate anomalies. To address these issues, we propose an Adaptive Prototype Guidance Network (APG-Net) for multi-sensor anomaly detection. First, to avoid cross-modal feature interference, we construct independent anomaly detection branches for multi-sensor data including RGB images, point clouds and infrared images. Then, we introduce a non-parametric feature space reshaping paradigm for each modality. This paradigm adds no additional trainable parameters, ensuring efficiency and ease of deployment. It first learns guiding prototypes from normal samples and matches them to the general representations produced by pretrained extractors. Subsequently, a two-stage adaptive prototype guidance strategy is applied to reshape the feature distributions. This strategy enlarges the separation between normal and anomalous features in the feature space. Finally, we perform decision-level fusion to integrate the anomaly detection strengths from all sensor data. Extensive experiments demonstrate that our method achieves an object-AUROC of 97.4% on the MulSen-AD multi-sensor anomaly detection benchmark, surpassing previous state-of-the-art approaches.