A Generative Model of Underwater Images for Active Landmark Detection and Docking

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 海洋信息技术装备中心

	A Generative Model of Underwater Images for Active Landmark Detection and Docking
	Liu S(刘爽)1,2,5; Ozay, Mete 4; Xu HL(徐红丽)2; Lin Y(林扬)1,2,5; Okatani, Takayuki 3
	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
内容类型	会议论文
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.University of Chinese Academy of Sciences, Beijing, China 2.Shenyang Institute of Automation, Chinese Academy of Sciences, State Key Laboratory of Robotics, Shenyang, China 3.RIKEN Center for Advanced Intelligence Project, Tokyo, Japan 4.Tohoku University, Graduate School of Information Sciences, Sendai, Miyagi, Japan 5.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
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.