Deep learning-based visual ensemble method for high-speed railway catenary clevis fracture detection-Reference-Cited by-同舟云学术

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Deep learning-based visual ensemble method for high-speed railway catenary clevis fracture detection

Published:2020-07 Issue: Volume:396 Page:556-568
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Han Ye^ORCID,Liu Zhigang^ORCID,Lyu Yang,Liu Kai,Li Changjiang,Zhang Wenxuan

Funder

National Natural Science Foundation of China

China Railway Science and Technology Major Research Project

Sichuan Province Youth Science and Technology Innovation Team

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference39 articles.

1. Video-based dynamic stagger measurement of railway overhead power lines using rotation-invariant feature matching;Cho;IEEE Trans. Intell. Transp. Syst.,2015

2. A high-Precision detection approach for catenary geometry parameters of electrical railway;Liu;IEEE Trans. Instrum. Meas.,2017

3. Automatic fastener classification and defect detection in vision-based railway inspection systems;Feng;IEEE Trans. Instrum. Meas.,2014

4. Automated visual inspection of railroad tracks;Resendiz;IEEE Trans. Intell. Transp. Syst.,2013

5. A real-time visual inspection system for discrete surface defects of rail heads;Li;IEEE Trans. Instrum. Meas.,2012

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