As an important direction of remote sensing application, remote sensing image ship target detection has wide application value in marine monitoring, port management, national defense security and other fields. In recent years, the rapid development of deep learning technology provides a new solution for ship target detection. To systematically analyze the strengths and limitations of existing ship detection algorithms, improvement strategies for remote sensing image detection were comprehensively discussed, so as to provide powerful artificial intelligence technology support for the operation organization of various ships. Firstly, the development process of object detection algorithms based on deep learning was systematically summarized. Then, the advantages and disadvantages of the improvement measures at each stage were analyzed for the detection difficulties of remote sensing images. After that, CiteSpace was used to conduct keyword co-occurrence analysis of literature from CNKI (China National Knowledge Infrastructure) and Web of Science in the past 8 years, and the current research progress and field hotspots were discussed. In addition, combined with the latest YOLOv10 and YOLOv11 experimental results, the advantages and disadvantages of existing technologies were analyzed. Finally, based on the literature review and experimental results, the limitations of the existing techniques were summarized and future research directions were proposed.