GSFANet: Global Spatial–Frequency Attention Network for Infrared Small Target Detection

Infrared small target detection (IRSTD) has progressed significantly in spatial-domain learning. However, single-frame images’ limited spatial semantics impair discrimination between targets and similar noise while complicating integrity detection of large-scale target. To address these, we propose Global Spatial-Frequency Attention Network (GSFANet), which enhances the distribution difference between targets and noise from a frequency-domain perspective while preserving spatial information integrity. The core innovations consist of three modules: 1) Parametric Wavelet Downsampling (PWD), preserving small target details during frequency refinement to prevent feature fragmentation; 2) Hierarchical Gated Kernel Attention (HGKA), capturing cross-level frequency relationships through Cross-channel Kernel Attention (C²K) and maintaining spatial coherence via Cross-spatial Gate Attention (CSG), effectively bridging semantic gaps across layers; 3) Adaptive Frequency-Decoupled Fusion (AdaFD), dynamically fusing target-associated frequency components while suppressing noise. We further develop AdaFL Loss to balance multi-scale target gradients and stabilize training. Experiments on three benchmark datasets demonstrate GSFANet’s superior detection performance and enhanced segmentation robustness in complex scenarios compared to state-of-the-art methods. Our code will be made public at https://github.com/dengfa02/GSFANet_IRSTD.