PRNet: Parallel Refinement Network with Selective Feature Enhancement for Infrared Small Target Detection

Infrared small target detection is a key task in computer vision and plays a crucial role in military defense, aerospace, and maritime surveillance. However, it remains a challenging task due to the small target size, low contrast, and complex background noise. Traditional methods, despite the progress made, still suffer from limited robustness and generalization capabilities. On the other hand, deep learning-based methods may fail to accurately recognize deep small targets due to insufficient background noise suppression. For this reason, we propose a parallel refinement network (PRNet). This network uses MobileNet v3 as the backbone for feature extraction, and then we propose the selective feature enhancement module (SFEM) to selectively enhance the extracted features to effectively improve the representativeness of the target features while suppressing the background interference. Finally, we propose the parallel refinement module (PRM) to optimize and aggregate features at different levels to achieve high accuracy and enhance the robustness of object detection. Experimental results on the public dataset MDFA show that the proposed method outperforms the current state-of-the-art methods in terms of detection performance.