Abstract: Objectives The research on the detection and recognition technology of marine ships has important economic and strategic significance for conservation of maritime rights and marine safety. Methods To address the problem of low accuracy and high misdetection rates caused by small targets, densely distributed targets and other occlusions; this research work proposes a novel ship target detection and recognition method named YOLOv3-ship algorithm based on YOLOv3 algorithm. The feature extraction network of the YOLOv3 algorithm has been improved to enhance the feature extraction ability. Subsequently, the fusion method of Res2Net and SE-Net is adopted to enhance the robustness and generalization ability of the algorithm model and the output prediction scale of the model is expanded to effectively improve the accuracy of detection and recognition of small ship targets. By increasing the weight for small ship targets in the loss function, the misdetection rate of small ship targets is further reduced. Results Experimental results show that the mAP based on YOLOv3-ship algorithm improved 9.0% than YOLOv3 algorithm. Conclusions The proposed YOLOv3-ship algorithm can relatively well solve the problems of high detection omission rate caused by small ship targets, dense distribution, and occlusion in large–scale maritime scenarios.