Visual perception is an important component for autonomous navigation of unmanned surface vessels (USV), particularly for the tasks related to autonomous inspection and tracking. These tasks involve vision-based navigation techniques to identify the target for navigation. Reduced visibility under extreme weather conditions in marine environments makes it difficult for vision-based approaches to work properly. To overcome these issues, this paper presents an autonomous vision-based navigation framework for tracking target objects in extreme marine conditions. The proposed framework consists of an integrated perception pipeline that uses a generative adversarial network (GAN) to remove noise and highlight the object features before passing them to the object detector (i.e., YOLOv5). The detected visual features are then used by the USV to track the target. The proposed framework has been thoroughly tested in simulation under extremely reduced visibility due to sandstorms and fog. The results are compared with state-of-the-art de-hazing methods across the benchmarked MBZIRC simulation dataset, on which the proposed scheme has outperformed the existing methods across various metrics.

翻译：视觉感知是无人水面艇自主导航的重要组成部分，尤其是自主检测与跟踪任务。这些任务依赖基于视觉的导航技术来识别跟踪目标。极端天气条件下海洋环境能见度降低，使得基于视觉的方法难以正常工作。为克服上述问题，本文提出一种面向极端海洋环境目标跟踪的自主视觉导航框架。该框架包含集成感知流水线，通过生成对抗网络（GAN）去除噪声并突出目标特征，随后将处理后的特征输入目标检测器（即YOLOv5）。检测到的视觉特征进而用于无人水面艇跟踪目标。该框架已在沙尘暴与雾导致的极端低能见度条件下通过仿真进行充分测试，并将结果与基准MBZIRC仿真数据集上的最新去雾方法进行了对比。实验结果表明，所提方案在多项指标上均优于现有方法。