No matter in the field of military investigation or maritime law enforcement, the ship target detection and identification technology is very important. Especially with the development of optical remote sensing satellite, a large number of ship imaging data have been obtained, and how to quickly and accurately locate and identify ship targets from a large number of optical imaging data is a challenging work. Firstly, this paper summarizes the development process and technical process of ship target detection and recognition technology in optical remote sensing images. Then, the research progress in the acquisition and preprocessing of optical remote sensing images, the separation of land and sea, the detection of ship targets and the recognition of ship targets are reviewed, and the methods and progress of target detection and recognition of optical remote sensing images using traditional and deep learning methods are emphatically discussed. Then, 11 kinds of remote sensing image data sets including ship targets and performance evaluation indexes are introduced. Finally, the main problems in ship target detection and recognition technology are analyzed, and the future development direction of ship target detection and recognition technology is given.

Unmanned Aerial Vehicles (UAV) have been widely used due to their advantages of small size, light weight, and simple operation. The combination of the deep learning method and UAV system can help to quickly and accurately detect the desired target on UAV aerial images with high definition and a wide field of view. Related topics have become one of the current research hotspots. This paper reviews the research progress of deep learning methods for object detection and semantic segmentation in UAV aerial images in the past ten years. First, the characteristics and wide range of application scenarios of UAVs and their aerial images are summarized, and the development process of target detection and semantic segmentation methods for UAV aerial images is briefly described. Then, the object detection and semantic segmentation methods of UAV aerial images based on deep learning are classified according to different network models, and their improvement strategies, application scenarios, contributions, and limitations are summarized. Subsequently, the data sets of aerial images taken by UAVs in recent years are collected and sorted out, and the evaluation indicators of commonly used convolutional neural network models are summarized. Finally, the existing problems in this field are pointed out, and prospects for future research trends are presented.