Vision-Based Autonomous Navigation for Unmanned Surface Vessel in
  Extreme Marine Conditions
Muhayyuddin Ahmed Ahsan Baidar Bakht Taimur Hassan Waseem Akram Ahmed Humais Lakmal Seneviratne Shaoming He Defu Lin Irfan Hussain
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.