文章基本信息

标题：Realistic Sonar Image Simulation Using Generative Adversarial Network
本地全文：下载
作者：Minsung Sung ; Jason Kim ; Juhwan Kim 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2019
卷号：52
期号：21
页码：291-296
DOI：10.1016/j.ifacol.2019.12.322
语种：English
出版社：Elsevier
摘要：Sonar sensors are widely utilized underwater because they can observe long-ranged objects and are tolerant to measurement conditions, such as turbidity and light conditions. However, sonar images have low quality and hard to collect, so development and application of sonar-based algorithms are difficult. This paper proposes a method to generate realistic sonar images or to segment real sonar image, to better utilize the sonar sensors. A simple sonar image simulator was implemented using a ray-tracing method. The simulator could calculate semantic information of real sonar images including properties of highlight, background, and shadow regions. Then, a generative adversarial network translated the simulated images into more realistic images or real sonar images into simulated-like images. The proposed method can be used to augment or pre-process sonar images.
关键词：KeywordsSonar simulatorForward Scan SonarFSSGANSonar GAN