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Ocean image data augmentation in the USV virtual training scene

机译：海洋图像数据增强在USV虚拟培训场景中

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摘要

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.
机译：智能导航的快速发展推动了海洋数据的快速积累，海洋科学已进入大数据的时代。但是，海洋环境的复杂性和可变性使一些数据不可用。它使海洋目标检测和无人面的曲面车辆（USV）在海洋情景中面临各种挑战，例如缺乏培训数据和培训环境。传统的海洋图像数据收集方法用于捕获复杂海洋环境的图像成本高昂，它导致海洋场景图像数据严重短缺。此外，自主学习环境的构建至关重要但耗时。为了解决上述问题，我们提出了一种使用虚拟海景和USV智能培训过程的数据收集方法。基于虚拟海景，我们在复杂的天气条件下获得了海洋场景的罕见图像，并实施了USV智能控制培训过程。实验结果表明，基于虚拟海景，改善了海洋目标检测的准确性和USV的障碍避免成功率。

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