A multi-category ship detection and recognition system based on YOLO algorithms

Many recent research papers have focused on improving the YOLO algorithm to enhance ship recognition accuracy and speed. However, little attention has been paid to the detection performance comparison between different versions of the original YOLO algorithms. Moreover, current ship image sets are either too few in categories or are aerial images and satellite remote sensing images which are unsuitable for Maritime Safety Administration. Based on the actual needs of the maritime department, we curated a dataset, ShipForMSA, containing 16 ship types with 9216 pictures of real-life photographs in total. We compared and analyzed the performance of five commonly used YOLO algorithms on the dataset by using Grad-CAM. We also designed a YOLO algorithm-based ship detection and recognition system with a recognition accuracy of 95.75%.