TGMNet: Temporal-Guided Mamba Network for Moving Infrared Dim and Small Target Detection

Infrared dim and small target detection has drawn increasing attention due to its importance in both military and civil applications. Multi-frame approaches exploit temporal information across consecutive frames and generally achieve better performance than single-frame methods. However, effective exploitation of temporal information while maintaining computational efficiency remains challenging. In this paper, we propose a Temporal-Guided Mamba Network (TGMNet) for moving infrared dim–small target detection. Specifically, a Spatially-Enhanced Temporal Mamba (SETM) module is designed to model temporal dependencies across consecutive frames. Prior to temporal modeling, Center Difference Convolution and a Multi-Scale Block are introduced to enhance local contrast and multi-scale spatial contextual information. A Feature Aggregation Module (FAM) is developed to integrate temporal motion features from SETM with complementary spatial information from the key and the reference frames, enabling effective spatiotemporal feature interaction. Extensive experiments on the DAUB and the IRDST datasets demonstrate that the proposed TGMNet achieves superior detection performance compared with state-of-the-art methods while maintaining high computational efficiency, validating its effectiveness for moving infrared dim–small target detection.