论文标题
合成声纳图像模拟具有各种海床条件的自动目标识别
Synthetic Sonar Image Simulation with Various Seabed Conditions for Automatic Target Recognition
论文作者
论文摘要
我们提出了一种新的方法来生成水下对象图像，该图像符合使用虚幻发动机由侧扫声纳产生的方法。我们描述了开发，调整和生成图像的过程，以提供代表性的图像，以用于训练自动化目标识别（ATR）和机器学习算法。这些方法为声学效应（例如后丝噪声和声学阴影）提供了视觉近似，同时允许在UE中使用C ++ Actor快速渲染，以最大程度地提高潜在的ATR训练数据集的大小。此外，我们还将分析其效用，以替代实际声纳图像或基于物理的声纳数据。
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.
