Research on the Automatic Detection of Ship Targets Based on an Improved YOLO v5 Algorithm and Model Optimization

Abstract

Because of the vast ocean area and the large amount of high-resolution image data, ship detection and data processing have become more difficult. These difficulties can be solved using the artificial intelligence interpretation method. The efficient and accurate detection ability of ship target detection has been widely recognized with the increasing application of deep learning technology. It is widely used in the practice of ship target detection. Firstly, we set up a data set concerning ship targets by collecting and training a large number of images. Then, we improved the YOLO v5 algorithm. The feature specify module (FSM) is used in the improved algorithm. The improved YOLO v5 algorithm was applied to ship detection practice under the framework of Anaconda. Finally, the training results were optimized, and the false alarm rate was reduced. The detection rate was improved. According to the statistics pertaining to experimental results with other algorithm models, the improved YOLO v5 algorithm can effectively suppress conflicting information, and the detection ability of ship details is improved. This work has accumulated valuable experience for related follow-up research.

Keywords

• automatic detection
• ship target
• improved YOLO v5 algorithm
• model optimization
• remote sensing technology