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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages8034-8039
Number of pages6
ISBN (Electronic)9781728140049
DOIs
Publication statusPublished - 2019 Nov
Event2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019 - Macau, China
Duration: 2019 Nov 32019 Nov 8

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)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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