A Generative Model of Underwater Images for Active Landmark Detection and Docking
文献类型：会议论文
| 作者 | Liu S(刘爽)1,2,3; Ozay, Mete4; Xu HL(徐红丽)1 ; Lin Y(林扬)1,2,3; Okatani, Takayuki5
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| 出版日期 | 2019 |
| 会议日期 | November 3-8, 2019 |
| 会议地点 | Macau, China |
| 页码 | 8034-8039 |
| 英文摘要 | Underwater active landmarks (UALs) are widely used for short-range underwater navigation in underwater robotics tasks. Detection of UALs is challenging due to large variance of underwater illumination, water quality and change of camera viewpoint. Moreover, improvement of detection accuracy relies upon statistical diversity of images used to train detection models. We propose a generative adversarial network, called Tank-to-field GAN (T2FGAN), to learn generative models of underwater images, and use the learned models for data augmentation to improve detection accuracy. To this end, first a T2FGAN is trained using images of UALs captured in a tank. Then, the learned model of the T2FGAN is used to generate images of UALs according to different water quality, illumination, pose and landmark configurations (WIPCs). In experimental analyses, we first explore statistical properties of images of UALs generated by T2FGAN under various WIPCs for active landmark detection. Then, we use the generated images for training detection algorithms. Experimental results show that training detection algorithms using the generated images can improve detection accuracy. In field experiments, underwater docking tasks are successfully performed in a lake by employing detection models trained on datasets generated by T2FGAN. |
| 产权排序 | 1 |
| 会议录 | 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
 |
| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISSN号 | 2153-0858 |
| ISBN号 | 978-1-7281-4004-9 |
| WOS记录号 | WOS:000544658406061 |
| 源URL | [http://ir.sia.cn/handle/173321/26423]  |
| 专题 | 沈阳自动化研究所_海洋信息技术装备中心 |
| 通讯作者 | Liu S(刘爽); Ozay, Mete |
| 作者单位 | 1.Shenyang Institute of Automation, Chinese Academy of Sciences, State Key Laboratory of Robotics, Shenyang, China 2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China 3.University of Chinese Academy of Sciences, Beijing, China 4.Tohoku University, Graduate School of Information Sciences, Sendai, Miyagi, Japan 5.RIKEN Center for Advanced Intelligence Project, Tokyo, Japan |
| 推荐引用方式 GB/T 7714 | Liu S,Ozay, Mete,Xu HL,et al. A Generative Model of Underwater Images for Active Landmark Detection and Docking[C]. 见:. Macau, China. November 3-8, 2019. |
入库方式： OAI收割
来源：沈阳自动化研究所
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