Cyclegan-Based Realistic Image Dataset Generation For Forward-Looking Sonar

In this paper, we propose a novel method to generate realistic acoustic datasets for forward-looking sonars. A forward-looking sonar, which is also known as an acoustic camera, outperforms other imaging sensor when applied in underwater tasks as it can provide more accurate and detailed information about the environment, even in dark or turbid water. However, the difficulty and high cost of acquiring acoustic images in real experiments encourage researchers to consider the generation of simulated acoustic image datasets. In particular, deep learning-based methods demonstrated high performance in computer vision tasks, such as in object detection. However, a large dataset is necessary in most cases. In the proposed method, we first build a novel user-friendly acoustic image simulator based on 3D modeling software. Then, the CycleGAN is applied to generate realistic acoustic images based on the generated dataset from the simulator. The experimental results demonstrate that our method can generate effective and realistic acoustic datasets with relatively simple operations.