In ship detection of SAR images,the existing detection methods have the problems of low accuracy and low recall rate for ship detection because the ship targets are small and numerous in SAR images.To address the above problems,this paper proposes a ship target detection algorithm,Vessel-YOLO model for SAR images.Firstly,YOLOv8n is taken as the benchmark network,and a CASPP context space pyramid pooling structure is proposed to improve the capability of the model to extract features of different scales.Secondly,by improving the loss function of this model to Wise-IoU bounding box loss based on dynamic non-monotonic focusing mechanism,the models adaptability to different quality images is improved.The robustness and reliability of the model are verified by extensive experiments on the standard datasets of SAR-Ship-Dataset and SSDD.The experimental results show that,compared with YOLOv8n,Vessel-YOLO improves mAP0.5∶0.95 by 1.8 and 2.2 percentage points on the two datasets respectively,and the proposed model with higher accuracy outperforms existing SAR image ship detection models.