Edge-Prior Guided Dual-Branch Enhancement Network for Infrared Small Target Detection

Infrared small target detection remains challenging in applications such as long-range surveillance and early warning due to the fact that infrared images rely on thermal radiation, which results in limited texture cues and a low signal-to-noise ratio for the targets. Although recent deep networks have improved representation capability, they often exhibit two persistent limitations. Fine target details are gradually weakened through successive downsampling, and edge-related priors are not sufficiently exploited to stabilize target responses under background interference. To alleviate these issues, an Edge-Prior Guided Dual-Branch Enhancement Network (EGDENet) is proposed, a dual-branch framework that injects edge priors into feature learning for infrared small target detection. An auxiliary edge-aware branch is introduced to complement the main encoder–decoder stream. Specifically, a Multi-directional Sobel Edge Extraction (MSEE) module is designed to adaptively reweight multi-directional edge responses, thereby strengthening boundary-sensitive representations. Furthermore, a Difference-Aware Gated Fusion (DAGF) module leverages Gated Spatial Convolution to capture subtle variations in the features and employs depthwise separable convolution along with adaptive enhancement to effectively integrate the extracted edge information. In addition, an Edge Pixel Integration (EPI) Loss is present to couple edge sensitivity with pixel-wise supervision. This loss improves the edge sensitivity of infrared small targets. The proposed EGDENet is evaluated on three benchmark datasets: NUAA-SIRST, IRSTD-1K, and SIRST-Aug. The experimental results show that our method outperforms or matches the performance of state-of-the-art methods.