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A Generative Model of Underwater Images for Active Landmark Detection and Docking
Liu S(刘爽)1,2,3; Ozay, Mete4; Xu HL(徐红丽)1; Lin Y(林扬)1,2,3; Okatani, Takayuki5
Department海洋信息技术装备中心
Conference Name2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
Conference DateNovember 3-8, 2019
Conference PlaceMacau, China
Source Publication2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
PublisherIEEE
Publication PlaceNew York
2019
Pages8034-8039
Indexed ByEI ; CPCI(ISTP)
EI Accession number20201108295378
WOS IDWOS:000544658406061
Contribution Rank1
ISSN2153-0858
ISBN978-1-7281-4004-9
AbstractUnderwater 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.
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/26423
Collection海洋信息技术装备中心
Corresponding AuthorLiu S(刘爽); Ozay, Mete
Affiliation1.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
Recommended Citation
GB/T 7714
Liu S,Ozay, Mete,Xu HL,et al. A Generative Model of Underwater Images for Active Landmark Detection and Docking[C]. New York:IEEE,2019:8034-8039.
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