Side-Scan Sonar Image Simulation Considering Imaging Mechanism and Marine Environment for Zero-Shot Shipwreck Detection

In the process of side-scan sonar (SSS) image target detection and recognition, the direct application of deep learning techniques will cause serious overfitting due to the limited amount of sonar image sample data, thus restricting the accuracy of shipwreck detection and recognition. Therefore, based on the imaging mechanism and image characteristics of SSS, this article proposes a joint ray model and sonar equation method for the simulation of SSS image samples. Primarily, the sonar equation is used to quantify the energy loss of the sound wave propagation underwater, and a ray model is established to simulate the transmission path of the sound waves underwater to realize the simulation of the SSS image. In addition, to further enhance the realism of the simulated SSS image, a target-to-target and background-to-background improved style transfer method is proposed and combined with a noise-added model to achieve the simulation of the impact of the marine environment on imaging. The method is applied to SSS target detection and identification, based on the SSS simulation imaging and data enhancement method to generate shipwreck samples for the training of the shipwreck detection model and testing with real data. The experimental results indicate that the accuracy of the model trained entirely on the simulation samples is comparable to the accuracy of model detection for training based on real samples, which verifies the reliability of the method.