Convolutional neural network for detecting railway fastener defects using a developed 3D laser system-Reference-Cited by-同舟云学术

Convolutional neural network for detecting railway fastener defects using a developed 3D laser system

Published:2020-09-27 Issue:5 Volume:9 Page:424-444
ISSN:2324-8378
Container-title:International Journal of Rail Transportation
language:en
Short-container-title:International Journal of Rail Transportation

Author:

Zhan You¹^ORCID,Dai Xianxing²,Yang Enhui¹,Wang Kelvin C.P.³

Affiliation:

1. School of Civil Engineering, Southwest Jiaotong University, Chengdu, China

2. Design and Development Department, Sino-Ocean Group Holding Limited, Chengdu, China

3. School of Civil and Environmental Engineering, Oklahoma State University, Stillwater, OK, United States

Funder

China Postdoctoral Science Foundation

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Informa UK Limited

Subject

Mechanics of Materials,Transportation,Automotive Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/23248378.2020.1825128

Reference41 articles.

1. Deep Multitask Learning for Railway Track Inspection

2. Li C Research and implementation of visual perception enhancement algorithms based on illuminance-reflectance model. Dissertation, Beijing: Beijing Jiaotong University; 2012. [in Chinese]

3. A Nonballasted Rail Track Slab Crack Identification Method Using a Level-Set-Based Active Contour Model

4. Rail corrosion forensics using 3D imaging and finite element analysis

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