Synthetic Sonar Image Simulation with Various Seabed Conditions for Automatic Target Recognition
This work addresses the need for large, diverse training datasets in underwater object recognition, but it is incremental as it builds on existing simulation techniques.
The authors tackled the problem of generating realistic synthetic sonar images for training automated target recognition systems by developing a method using Unreal Engine to simulate acoustic effects like back-scatter noise and acoustic shadow, achieving fast rendering to maximize dataset size.
We propose a novel method to generate underwater object imagery that is acoustically compliant with that generated by side-scan sonar using the Unreal Engine. We describe the process to develop, tune, and generate imagery to provide representative images for use in training automated target recognition (ATR) and machine learning algorithms. The methods provide visual approximations for acoustic effects such as back-scatter noise and acoustic shadow, while allowing fast rendering with C++ actor in UE for maximizing the size of potential ATR training datasets. Additionally, we provide analysis of its utility as a replacement for actual sonar imagery or physics-based sonar data.