Multimodal Industrial Anomaly Detection via Attention-Enhanced Memory-Guided Network

Anomaly detection is a key technology in quality control for automated production lines. Currently, 2D-based anomaly detection methods fail to identify geometric structure anomalies in products. To address this limitation, this paper proposes a multimodal anomaly detection model using 3D point clouds and RGB images. To ensure the single-domain inference capability of each modality, we design an attention-enhanced dual memory bank to separately store local point cloud features and RGB features. The attention mechanism enhances the informativeness and discriminability of the feature descriptors, significantly improving the data quality in the memory bank. During the inference phase, the local point cloud features in the dual memory bank guide the RGB features in calculating anomaly scores in the 2D modality. This memory-guided approach strengthens the correlation between information across different modalities. Moreover, to improve the overall segmentation precision of the model, we propose an anomaly scoring scheme based on a weight map of signed distance values. The final anomaly detection results are obtained by integrating the advantages of point cloud data in geometric structure anomaly detection and RGB data in color anomaly detection. Extensive experiments demonstrate that the proposed method achieves superior segmentation precision compared to other advanced methods on the MVTec 3D-AD and Eyecandies datasets.