Deep learning-based robust positioning scheme for imaging sonar guided dynamic docking of autonomous underwater vehicle

In this paper, a deep learning-based underwater positioning scheme is proposed to achieve robust feature tracking of an autonomous underwater vehicle (AUV) in sonar image during dynamic docking. To address the issues that the distorted feature and acoustic noises lead significant difficulty to detection and tracking of AUV in acoustic image during dynamic docking, first, a pre-trained You Only Look Once (YOLO) network is applied to detect both body and head features of AUV. Second, we introduce an Intersection Over Union (IOU) match-based backend which preliminarily filters the error detections of AUV head based on the rigid relationship between body and head of AUV. Subsequently, Simple Online and Realtime Tracking with a deep association metric (DeepSort) is utilized to achieve track matching of all detection results including error detections and real target. Moreover, a scoring mechanism is presented to further remove the unfiltered error detections based on the motion tendency of detection tracks. Experiment result shows that the proposed scheme enables real-time and robust feature tracking of AUV with the interference of feature distortion, reverberation and environmental noises.