State Space Model and Self-Attention for Infrared Dual-Band Small Target Detection

Dual-band infrared small-target detection not only offers richer information than single-band methods but also poses two key challenges: modeling weak targets in cluttered scenes and robustly fusing the complementary features of the two bands. To overcome these challenges, we propose the state space model (SSM) and self-attention for infrared dual-band small target detectionn (s³-irdstd) framework. This framework first employs a novel dynamic attention module (DAM) that integrates state space modeling with self-attention, efficiently capturing long-range dependencies and global context to enhance target discriminability. Second, to ensure effective feature integration, we design a Fourier-based fusion module. Operating in the frequency domain, it avoids the information loss that can occur in direct spatial fusion, thereby yielding a more complete target representation. Experiments show that s3-irdstd significantly outperforms existing approaches, validating its effectiveness and innovative design. Further information is available on our project page: https://linaom1214.github.io/SSD