HFMM-Net: A Hybrid Fusion Mamba Network for Efficient Multimodal Industrial Defect Detection

With the increasing demand for higher precision and real-time performance in industrial surface defect detection, multimodal detection methods integrating RGB images and 3D point clouds have drawn considerable attention. However, current mainstream methods typically employ computationally expensive Transformer-based models for capturing global features, resulting in significant inference delays that hinder their practical deployment for online inspection tasks. Furthermore, existing approaches exhibit limited capability in deep cross-modal interactions, negatively impacting defect detection and segmentation accuracy. In this paper, we propose a novel multimodal anomaly detection framework based on a bidirectional Mamba network to enhance cross-modal feature interaction and fusion. Specifically, we introduce an anomaly-aware parallel feature extraction network, leveraging a hybrid scanning state space model (SSM) to efficiently capture global and long-range dependencies with linear computational complexity. Additionally, we develop a cross-enhanced feature fusion module to facilitate dynamic interaction and adaptive fusion of multimodal features at multiple scales. Extensive experiments conducted on two publicly available benchmark datasets, MVTec 3D-AD and Eyecandies, demonstrate that the proposed method consistently outperforms existing approaches in both defect detection and segmentation tasks.