Image stitching and target perception for Autonomous Underwater Vehicle-collected side-scan sonar images

Introduction Autonomous Underwater Vehicles (AUVs) are capable of independently performing underwater navigation tasks, with side-scan sonar being a primary tool for underwater detection. The integration of these two technologies enables autonomous monitoring of the marine environment. Methods To address the limitations of existing seabed detection methods, such as insufficient robustness and high complexity, this study proposes a comprehensive seabed detection method based on a sliding window technique. Additionally, this study introduces a sonar image stitching method that accounts for variations in image intensity and addresses challenges arising from multi-frame overlaps and gaps. Furthermore, an autonomous target perception framework based on shadow region segmentation is proposed, which not only identifies targets in side-scan sonar images but also provides target height measurements. Results Comprehensive seabed detection method improves accuracy by 31.2% compared to the peak detection method. In experiments, the height measurement error for this method was found to be 9%. Discussion To validate the effectiveness of the proposed seabed detection method, sonar image stitching method, and target perception framework, comprehensive experiments were conducted in the Qingjiang area of Hubei Province. The results obtained from the lake environment demonstrated the effectiveness of the proposed methods.