Realistic Sonar Image Simulation Using Generative Adversarial Network
- Title
- Realistic Sonar Image Simulation Using Generative Adversarial Network
- Authors
- Sung, M.; Kim, J.; Kim, J.; Yu, S.-C.
- Date Issued
- 2019-09-19
- Publisher
- Elsevier B.V.
- Abstract
- 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. © 2019. The Authors. Published by Elsevier Ltd. All rights reserved.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/112997
- ISSN
- 2405-8963
- Article Type
- Conference
- Citation
- 12th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles CAMS 2019, page. 291 - 296, 2019-09-19
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