Frequency–Spatial Collaborative Gated Attention Network for Infrared Small Target Detection

Abstract

Infrared small target detection (IRSTD) is challenged by low signal-to-noise ratios and complex background clutter. Existing methods remain insufficient in capturing spectral discrepancies and fusing dual-domain features. To address these limitations, we propose FSGANet, which improves frequency-domain clutter suppression through wavelet priors and adopts gated attention to blend dual-domain features, thereby significantly enhancing IRSTD performance. In particular, the model consists of three modules: 1) frequency-spatial collaborative gated attention (FSCGA) module, a dual-domain fusion attention module that captures multiscale spatial features and global frequency-domain information, and blends dual-domain representations to construct target features while suppressing irrelevant noise; 2) dynamic frequency refine (DFR) module, which employs compression, dynamic weight assignment, and recovery to further amplify the spectral signals of targets and attenuate clutter spectral components; and 3) encoder-aligned wavelet prior (EAWP), a wavelet-transform-based prior spectral cue that guides the frequency-domain selection block in FSCGA to precisely capture foreground–background spectral discrepancies. FSGANet contains 1.79-M parameters and 32.33 GFLOPs. Extensive experiments on four public datasets demonstrate the superior detection performance and efficiency of our model. The code is available at https://github.com/xiaodacheng01/FSGANet