SR-Unet:用于红外小目标检测的切分滚动网络OA

To address the challenges faced by current CNN-based infrared small-target detection methods in capturing long-range dependencies,as well as the high computational complexity and poor local feature learning of transformer-based methods,this study proposes an infrared small-target detection network algorithm called Split Rolling-Unet(SR-Unet)that combines MLP and CNN.Based on the Rolling-Unet,this algorithm adds a multiscale deep supervision fusion.By constructing the MSORMLP and LIEM,multidirectional long-range dependencies were captured while integrating the local context information.Comparative experiments were conducted on the public NUAA-SIRST and NUDT-SIRST datasets.The results demonstrate that SR-Unet contains only 2.01 M parameters while outperforming current mainstream infrared small-target detection algorithms across multiple evaluation metrics.Ablation experiments showed that the improved algorithm increased IoU from 0.7463 to 0.7851,F1 score from 0.8547 to 0.8796,and Pd from 93.92%to 96.2%.SR-Unet demonstrates higher detection accuracy,a higher detection probability,and overall superior performance in infrared small-target detection tasks.