Comprehensive Sample Augmentation by Fully Considering SSS Imaging Mechanism and Environment for Shipwreck Detection Under Zero Real Samples

Abstract

To solve the shortage of training samples when using deep learning to detect shipwrecks, a comprehensive sample augmentation method is proposed. The method fully considers the imaging mechanism and environment of side-scan sonar (SSS), such as acoustic emission and reception, waterbody, target reflection, and seafloor background, and generates diverse and representative shipwreck samples from five aspects of target diversity, target texture, imaging resolution, equipment and environmental noises, and background through a series of novel sample augmentation methods. Under the condition of zero real SSS samples, a detection model of YOLOv5s was established with these amplified samples and achieved a mean average precision (MAP) better than 96% for real SSS data detection.