A Gradient-Compensated Feature Learning Network for Infrared Small Target Detection

Infrared small target detection under complex backgrounds remains challenging due to the extremely small target size and low contrast with the surrounding background. These factors make contour information difficult to extract and often cause target features to attenuate or disappear during deep feature learning. To address these issues, this paper proposes a Gradient-Compensation-based Feature Learning Network (GCFLNet). GCFLNet adopts a multi-module collaborative design to enhance feature representation and fusion. First, an Edge Enhancement Module (EEM) is introduced to accurately capture fine-grained edge information of infrared small targets while suppressing background noise through smoothing operations. This provides reliable structural cues for subsequent feature extraction. Second, the extracted edge features are embedded into a Global–Local Feature Interaction (GLFI) module, which is inspired by self-attention mechanisms with dilated convolutions to strengthen global semantic dependencies and local detail representation, enabling effective enhancement of target features. In addition, a Multi-Scale Information Compensation (MSIC) module is designed to exploit the complementary characteristics of multi-scale features across spatial and channel dimensions, guiding efficient fusion of high-level and low-level information. Experimental results on the NUDT and IRSTD-1K datasets demonstrate that GCFLNet outperforms existing state-of-the-art methods, achieving higher detection accuracy and robustness for infrared small targets in complex backgrounds.