Contour Accentuation for Transfer Learning-Based Ship Recognition Method

Chi Hua Chen, Yizhuo Zhang, Wenzhong Guo, Mingyang Pan, Lingjuan Lyu, Chia Yu Lin

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

4 Scopus citations

Abstract

This study proposes a ship recognition system which includes intelligent bridge piers and a ship recognition server. The ship recognition server can analyse the contour features of ship images from intelligent bridge piers by the proposed contour accentuation method; the ship image with contour accentuation can be adopted as the inputs of transfer learning-based neural network for ship classification by the proposed transfer learning-based ship recognition method. In practical experiments, the results showed that the proposed transfer learning-based ship recognition method with contour accentuation can obtain higher accuracy, and the accuracy of the proposed method was 97.79%.

Original languageEnglish
Title of host publicationThe Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020
PublisherAssociation for Computing Machinery
Pages61-62
Number of pages2
ISBN (Electronic)9781450370240
DOIs
StatePublished - 20 Apr 2020
Event29th International World Wide Web Conference, WWW 2020 - Taipei, Taiwan
Duration: 20 Apr 202024 Apr 2020

Publication series

NameThe Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020

Conference

Conference29th International World Wide Web Conference, WWW 2020
Country/TerritoryTaiwan
CityTaipei
Period20/04/2024/04/20

Keywords

  • contour accentuation
  • convolutional neural network
  • ship recognition
  • transfer learning

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