SCMT-Net: Spatial Curvature and Motion Temporal Feature Synergy Network for Multi-Frame Infrared Small Target Detection

Infrared small target (IRST) detection remains a challenging task due to extremely small target sizes, low signal-to-noise ratios (SNR), and complex background clutter. Existing methods often fail to balance reliable detection with low false alarm rates due to limited spatial–temporal modeling. To address this, we propose a multi-frame network that synergistically integrates spatial curvature and temporal motion consistency. Specifically, in the single-frame stage, a Gaussian Curvature Attention (GCA) module is introduced to exploit spatial curvature and geometric saliency, enhancing the discriminability of weak targets. In the multi-frame stage, a Motion-Aware Encoding Block (MAEB) utilizes MotionPool3D to capture temporal motion consistency and extract salient motion regions, while a Temporal Consistency Enhancement Module (TCEM) further refines cross-frame features to effectively suppress noise. Extensive experiments demonstrate that the proposed method achieves advanced overall performance. In particular, under low-SNR conditions, the method improves the detection rate by 0.29% while maintaining a low false alarm rate, providing an effective solution for the stable detection of weak and small targets.