CycleGAN-based realistic image dataset generation for forward-looking sonar
CycleGAN-based realistic image dataset generation for forward-looking sonar
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DOI：
10.1080/01691864.2021.1873845
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发表时间：
2021-01-20
影响因子：
2
通讯作者：
Asama, Hajime
中科院分区：
文献类型：
--
作者：
Liu, Dingyu;Wang, Yusheng;Asama, Hajime
本文提出了一种为前视声纳生成逼真声学数据集的新方法。前视声纳，也被称为声相机，在应用于水下任务时性能优于其他成像传感器，因为它可以提供关于环境的更准确和详细的信息，即使在黑暗或浑浊的水中也是如此。然而，在实际实验中获取声像的难度和高昂的成本促使研究人员考虑生成模拟声像数据集。特别是，基于深度学习的方法在计算机视觉任务中表现出了高性能，例如在目标检测中。然而，在大多数情况下，大型数据集是必需的。在该方法中，我们首先基于3D建模软件构建了一个用户友好的新型声像模拟器。然后，基于从模拟器生成的数据集，应用CycleGAN来生成逼真的声学图像。实验结果表明，该方法能够以相对简单的操作生成有效的、逼真的声学数据集。
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.