In recent years, synthetic aperture radar imaging technology (SAR) has played an important role in the real-time monitoring and control of the ocean due to its all-time and all-weather target sensing capabilities. In particular, the detection of ship targets in high-resolution SAR images has become current one of the research hotspots. First, the process of ship target detection based on deep learning in SAR images is analyzed, and the key steps such as the construction of sample training datasets are summarized, the extraction of target features and the design of target frame selection. Then, the influence of each part of the detection process on the detection accuracy and speed of the ship target in the SAR image is compared and analyzed. Finally, according to the current research status, the problems of deep learning algorithms in the application of ship detection are deeply analyzed, and the further research direction of ship target detection based on deep learning in SAR images is discussed.