A Gradient Vector Self-Learning Network for Infrared Small Target Detection

Infrared small target detection (IRSTD) is challenging due to low target-background contrast and small target size, leading to missed detections and false alarms (Fas). To address these problems, a novel gradient vector self-learning network (GVSLNet) is proposed. First, the self-learning gradient vector (SLGV) module is designed based on the unique high correlation of infrared small target gradient. Traditional gradient vector field operators cannot update and learn the deep features. SLGV overcomes these limitations by using CNN to adaptively learn and calculate the gradient vector of infrared images, improving the ability to distinguish between targets and backgrounds under complex environments. Then, edge features are encoded into the global-local attention fusion (GLAF) module, which is based on Transformer and dilated convolution. Infrared small target images often display nonlocal self-similarity, where background signals tend to share similar structures. The GLAF module leverages this characteristic to further enhance target intensity while effectively suppressing background noise. The proposed GVSLNet can dynamically calculate gradients based on scene information, improving the detection ability of small targets in complex environments. Experimental results prove that the proposed GVSLNet outperforms state-of-the-art methods on public datasets while maintaining high inference speed.