Real-Time Automatic Wall Detection and Localization based on Side Scan Sonar Images

Abstract

Accurate identification of an uncertain underwater environment is one of the challenges of underwater robotics. Autonomous Underwater Vehicle (AUV) needs to understand its environment accurately to achieve autonomous tasks. The method proposed in this paper is a real-time automatic target recognition based on Side Scan Sonar images to detect and localize a harbor’s wall. This paper explains real-time Side Scan Sonar image generation and compares three Deep Learning object detection algorithms (YOLOv5, YOLOv5-TR, and YOLOX) using transfer learning. The YOLOv5-TR algorithm has the most accurate detection with 99% during training, whereas the YOLOX provides the best accuracy of 91.3% for a recorded survey detection. The YOLOX algorithm realizes the flow chart validation’s real-time detection and target localization.