Learning deep representations and detection of docking stations using underwater imaging

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

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

1 Citation (Scopus)

Abstract

Underwater docking endows AUVs with the ability of recharging and data transfer. Detection of underwater docking stations is a crucial step required to perform a successful docking. We propose a method to detect underwater docking stations using two dimensional images captured under different environmental light variance, deformations aroused by scale and rotation, different light intensity and partial observation. In order to realize our proposed method, we first train Convolutional Neural Networks (CNNs) to learn feature representations and then employ a deep detection network. In order to analyze the performance of the proposed method, we prepared an image dataset of docking stations using underwater imaging. Then, we explore the performance of our method using different data augmentation methods. We improved the AUC of detection by 0.14 using data augmentation and obtained 0.88 AUC with data augmentation. An increment of 0.23 AUC is gained by transfer learning and we obtained 0.88 AUC on another datasets.

Original languageEnglish
Title of host publication2018 OCEANS - MTS/IEEE Kobe Techno-Oceans, OCEANS - Kobe 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538616543
DOIs
Publication statusPublished - 2018 Dec 4
Event2018 OCEANS - MTS/IEEE Kobe Techno-Oceans, OCEANS - Kobe 2018 - Kobe, Japan
Duration: 2018 May 282018 May 31

Publication series

Name2018 OCEANS - MTS/IEEE Kobe Techno-Oceans, OCEANS - Kobe 2018

Other

Other2018 OCEANS - MTS/IEEE Kobe Techno-Oceans, OCEANS - Kobe 2018
Country/TerritoryJapan
CityKobe
Period18/5/2818/5/31

Keywords

  • CNNs
  • Detection
  • Underwater docking
  • Underwater imaging

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Oceanography
  • Space and Planetary Science
  • Energy Engineering and Power Technology
  • Ocean Engineering
  • Acoustics and Ultrasonics
  • Instrumentation

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