Attention Shift-Invariant Cross-Evolutionary Feature Fusion Network for Infrared Small Target Detection

Infrared images typically exhibit diverse backgrounds, each potentially containing noise and target-like interference elements. In complex backgrounds, infrared small targets are prone to be submerged by background noise due to their low pixel proportion and limited available features, leading to detection failure. To address this problem, this paper proposes an Attention Shift-Invariant Cross-Evolutionary Feature Fusion Network (ASCFNet) tailored for the detection of infrared weak and small targets. The network architecture first designs a Multidimensional Lightweight Pixel-level Attention Module (MLPA), which alleviates the issue of small-target feature suppression during deep network propagation by combining channel reshaping, multi-scale parallel subnet architectures, and local cross-channel interactions. Then, a Multidimensional Shift-Invariant Recall Module (MSIR) is designed to ensure the network remains unaffected by minor input perturbations when processing infrared images, through focusing on the model’s shift invariance. Subsequently, a Cross-Evolutionary Feature Fusion structure (CEFF) is designed to allow flexible and efficient integration of multidimensional feature information from different network hierarchies, thereby achieving complementarity and enhancement among features. Experimental results on three public datasets, SIRST, NUDT-SIRST, and IRST640, demonstrate that our proposed network outperforms advanced algorithms in the field. Specifically, on the NUDT-SIRST dataset, the mAP50, mAP50-95, and metrics reached 99.26%, 85.22%, and 99.31%, respectively. Visual evaluations of detection results in diverse scenarios indicate that our algorithm exhibits an increased detection rate and reduced false alarm rate. Our method balances accuracy and real-time performance, and achieves efficient and stable detection of infrared weak and small targets.