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

Shuang Liu, Mete Ozay, Hongli Xu, Yang Lin, Takayuki Okatani

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトル2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ページ8034-8039
ページ数6
ISBN(電子版)9781728140049
DOI
出版ステータスPublished - 2019 11
イベント2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019 - Macau, China
継続期間: 2019 11 32019 11 8

出版物シリーズ

名前IEEE International Conference on Intelligent Robots and Systems
ISSN(印刷版)2153-0858
ISSN(電子版)2153-0866

Conference

Conference2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
CountryChina
CityMacau
Period19/11/319/11/8

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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