MSFANet: Infrared Small Target Detection Based on Multiscale Frequency- and Spatial-Domain Attention Network

Infrared small target detection (ISTD) is crucial for civilian and military applications, but accurately identifying weak targets amid complex backgrounds remains challenging. To address this, we propose MSFANet, a multi-scale frequency-and spatial-domain attention network. MSFANet employs a multi-scale mixed attention module for effective feature extraction and fusion, and introduces a dynamic frequency-spatial module (DFSM)， which specifically enhances high-frequency signals by Fourier transform of high-level features, preserves details by combining spatial convolution of low-level features, and dynamically fuses them to suppress background noise. Deep supervision and a combined loss strategy further improve segmentation accuracy and robustness. Experiments show MSFANet outperforms state-of-the-art (SOTA) methods on challenging infrared datasets.