Abstract: Autonomous detection of underwater shipwreck based on side scan sonar (SSS) is the key research direction of underwater archaeology. The limited quantity of underwater targets hinders the training of the detectors. To address this issue, we employ artificial intelligence-generated content (AIGC) technology based on stable diffusion model (SDM) to supplement scarce SSS target instances. By comparing the effects of various generative techniques, we demonstrate the potential of AIGC technology in the field of underwater shipwreck detection. Based on this technique, we propose a data enhancement method without additional optical data and manual annotation, called automatic diffusion generation (ADG), which can be used to achieve high precision underwater shipwreck detection. On the YOLOv8n, the detector trained with this method can achieve 95.0% precision and 96.3% recall for underwater shipwreck detection, exceeding 1.5% and 1.8% of the detector trained with only the original data. On the Faster RCNN, this method can also further improve the detector accuracy, achieving 94.8% precision and 97.3% recall.

Key words: di?usion model, object detection, side-scan sonar, underwater shipwreck detection