The detection of naval targets is a key area of research interest in the field of remote sensing image processing and pattern recognition. Moreover， the automatic detection of naval targets is crucial to both civil and military applications. In this study， we discuss and analyze the advantages and disadvantages of typical deep-learning-based target-detection algorithms， compare and summarize them， and summarize state-of-the-art deep-learning-based ship target detection methods. We also provide a detailed introduction to five aspects of state-of-the-art ship target detection methods， including multi-scale detection， multi-angle detection， small target detection， model light-weighting， and large-format wide remote sensing imaging. We also introduce the common evaluation criteria of ship target recognition algorithms and existing ship image datasets， and discuss the current problems faced by ship target detection algorithms using remote sensing images and future development trends in the field.