Accurate ship target detection technology can improve the omni-directional perception ability of weapons and equipment.Aiming at the serious problem of false alarm and missing alarm in SAR ship target detection in complex environment,a ship target detection algorithm 3S-YOLO based on YOLOv5 in lightweight SAR image is proposed.Firstly,3S-YOLO algorithm reconstructs the network structure,adjusts the relationship between receptive field and multi-scale fusion,and realizes the lightweight processing of feature extraction network and feature fusion network.Then,the network is pruned,and compressed by FPGM pruning algorithm to speed up the reasoning.Finally,the network is trained with varifocal loss to make IACS regression.The results show that the accuracy of the algorithm can be improved to 99.1% after optimization.After pruning,the volume of the model is greatly reduced,which can be compressed to 190 kiB,a decrease of 98.6%.The reasoning speed of the algorithm is increased by 4 times,and the reasoning time is reduced to less than 3 ms.Compared with the current mainstream algorithms,3S-YOLO has achieved good results in all aspects,which can meet the real-time ship target detection in SAR images.