基于优化YOLOv8的SAR图像舰船目标检测算法OA

To address the problems of noise interference and poor performance in detecting small tar-gets in ship target detection in SAR images,the AD-YOLO algorithm is proposed.Based on YOLOv8,the algorithm introduces the ADNet for denoising before the target detection network to improve image qual-ity,replaces the C2f module in the backbone network with the DWR_C2f module to enhance the feature representation of multi-scale targets,and introduces the DAttention attention mechanism after the SPPF to adapt to complex scenarios.Experiments on the SARDet-100k dataset show that compared with the baseline model YOLOv8,AD-YOLO achieves improvements of 1.33 and 1.00 in mAP in the n and s model sizes respectively.Moreover,it exhibits stronger robustness against background noise,small tar-gets,scattering interference and other situations,thereby effectively enhancing the detection accuracy and robustness of ship targets in SAR images.