Local-Motion Mamba With Frequency-Guided for Moving Infrared Small Target Detection

Moving Infrared Small Target Detection (MISTD) remains challenging due to the large computational load caused by multi-frame input and background motion interference, leading to poor real-time and accuracy performance. To address these issues, we propose STLMamba, a spatial-temporal framework that integrates long-range dependency modeling with local feature sensitivity, guided by infrared video and frequency priors. Specifically, an Adaptive Multi-directional Frequency Enhancement (AMFE) module exploits spatial frequency cues to enhance targets, while an Efficient Local-motion Mamba (ELM) with a learnable Local Dynamic Gaussian Motion (LDGM) mechanism captures motion patterns and suppresses background noise. Experiments demonstrate that STLMamba achieves 43.14 FPS in NUDT-MIRSDT and 49.22 FPS in TSIRMT, outperforming state-of-the-art methods in both accuracy and efficiency.