Ocean image data augmentation in the USV virtual training scene

Abstract

The rapid development of intelligent navigation drives the rapid accumulation of ocean data, and the ocean science has entered the era of big data. However, the complexity and variability of the ocean environments make some data unavailable. It makes ocean target detection and the unmanned surface vehicle (USV) intelligent control process in ocean scenarios face various challenges, such as the lack of training data and training environment. Traditional ocean image data collection method used to capture images of complex ocean environments is costly, and it leads to a serious shortage of ocean scene image data. In addition, the construction of an autonomous learning environment is crucial but time-consuming. In order to solve the above problems, we propose a data collection method using virtual ocean scenes and the USV intelligent training process. Based on virtual ocean scenes, we obtain rare images of ocean scenes under complex weather conditions and implement the USV intelligent control training process. Experimental results show that the accuracy of ocean target detection and the success rate of obstacle avoidance of the USV are improved based on the virtual ocean scenes.