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Research on deep learning method for rail surface defect detection

  • Published:2020-11-18 Issue:4 Volume:10 Page:436-442
  • ISSN:2042-9738
  • Container-title:IET Electrical Systems in Transportation
  • language:en
  • Short-container-title:IET Electrical Systems in Transportation

Author:

Feng Jiang Hua1,Yuan Hao1ORCID,Hu Yun Qing1,Lin Jun1,Liu Shi Wang1,Luo Xiao1

Affiliation:

1. CRRC Zhuzhou Institute Co., LtdShidai RoadZhuzhouPeople's Republic of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering

Reference36 articles.

1. Review of railway rail defect non‐destructive testing and monitoring;Tian G.Y.;Chin. J. Sci. Instrum.,2016

2. Simulation on rail head crack initiation life prediction considering rail wear;Zhou Y.;J. of the China Railw. Soc.,2016

3. Rail surface defect detection based on visual attention and PLSA model;Wang J.;J. of Railw. Sci. Eng.,2015

4. Ministry of Railways of the People's Republic of China: ‘Rules of maintenance of railway lines’ 2001

5. Groundwork for Rail Flaw Detection Using Ultrasonic Phased Array Inspection

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