MLEDNet: Multi-Directional Learnable Edge Information-Assisted Dense Nested Network for Infrared Small Target Detection

Infrared small target detection (IRSTD) remains a critical yet challenging task due to the inherent low signal-to-noise ratio, weak target features, and complex backgrounds prevalent in infrared images. Existing methods often struggle to effectively capture the subtle edge features of targets and suppress background clutter simultaneously. To address these limitations, this study proposed a novel Multi-directional Learnable Edge-assisted Dense Nested Attention Network (MLEDNet). Firstly, we propose a multi-directional learnable edge extraction module (MLEEM), which is designed to capture rich directional edge information. The extracted multi-directional edge features are hierarchically integrated into the dense nested attention module (DNAM) to significantly enhance the model’s capability in discerning the crucial edge features of infrared small targets. Then, we design a feature fusion module guided by residual channel spatial attention (ResCSAM-FFM). This module leverages spatio-channel contextual cues to intelligently fuse features across different levels output by the DNAM, effectively enhancing target representation while robustly suppressing complex background interferences. By combining the MLEEM and the ResCSAM-FFM within a dense nested attention framework, we present a new model named MLEDNet. Extensive experiments conducted on benchmark datasets NUDT-SIRST and NUAA-SIRST demonstrate that the proposed MLEDNet achieves superior performance compared to state-of-the-art methods.