A Convolutional Neural Network Based on Double-tower Structure for Underwater Terrain Classification

Wei Liu, Rongxin Cui, Yang Li, Shuqiang Liu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Terrain classification plays a critical role in all robot systems especially in unknown environments. In recent years, researchers have proposed various algorithms to improve the efficiency and accuracy of terrain classification. Nevertheless, these methods still have some deficiencies in classification efficiency. In this paper, a double-tower convolutional neural network has been designed to implement end-to-end underwater terrain classification. The matched sonar image and visual image constitute an image pair, which is obtained at the same time by the sonar sensor and the visual sensor of the robot or underwater vehicle. The corresponding image pairs are set to be the input of the convolutional neural network, and the output of the network is the classification of the terrain. Then, terrain features from sonar and visual images are simultaneously applied to achieve terrain classification. Therefore, an end-to-end convolutional neural network with a classification function has been established in this paper.

Original languageEnglish
Title of host publicationICARM 2018 - 2018 3rd International Conference on Advanced Robotics and Mechatronics
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages697-702
Number of pages6
ISBN (Electronic)9781538670668
DOIs
StatePublished - 11 Jan 2019
Event3rd IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2018 - Singapore, Singapore
Duration: 18 Jul 201820 Jul 2018

Publication series

NameICARM 2018 - 2018 3rd International Conference on Advanced Robotics and Mechatronics

Conference

Conference3rd IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2018
Country/TerritorySingapore
CitySingapore
Period18/07/1820/07/18

Keywords

  • convolutional neural network
  • sonar image
  • terrain classification
  • visual image

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