Side-Scan Sonar Image Synthesis Based on CycleGAN with 3D Models and Shadow Integration

Side-scan sonar (SSS) is essential for acquiring high-resolution seafloor images over large areas, facilitating the identification of subsea objects. However, military security restrictions and the scarcity of subsea targets limit the availability of SSS data, posing challenges for Automatic Target Recognition (ATR) research. This paper presents an approach that uses Cycle-Consistent Generative Adversarial Networks (CycleGAN) to augment SSS images of key subsea objects, such as shipwrecks, aircraft, and drowning victims. The process begins by constructing 3D models to generate rendered images with realistic shadows from multiple angles. To enhance image quality, a shadow extractor and shadow region loss function are introduced to ensure consistent shadow representation. Additionally, a multi-resolution learning structure enables effective training, even with limited data availability. The experimental results show that the generated data improved object detection accuracy when they were used for training and demonstrated the ability to generate clear shadow and background regions with stability.