A Deep-Learning Approach for Visual Detection of an AUV Docking Station

Autonomous docking for underwater vehicles, especially locating the docking station, presents significant challenges for deploying sub-sea resident AUVs in exploration and monitoring tasks. To extend a fiducial marker-based docking station detection we propose to use the state-of-the-art object detection deep learning models, specifically YOLOv8 in various sizes. We assess the robustness of these models in detecting docking stations by training different model sizes under various configurations on a dataset collected at the DFKI test basin in Bremen, Germany. To show and improve their performances in a real-world under-ice scenario we utilize a previously recorded dataset from Torneträsk lake in Abisko, Sweden [1]. The performance of these models is then compared to the previously used fiducial marker-based docking station detection. Our results show that a combination of both the classical detection method with one of the trained YOLOv8 models improves the detection performance significantly.