Chinese Journal of Liquid Crystals and Displays, Volume. 38, Issue 5, 666(2023)

Rail surface crack detection algorithm based on improved YOLOv5s

Miao-sen ZHOU1,2, Quan-wu TANG1,2、*, Tian-tian SHI1,2, Tong-lan LUO1,2, Ze-xin ZHANG1,2, and Yong-xia XUE1,2
Author Affiliations
  • 1School of Physics and Electronic-Electrical Engineering,Ningxia University,Yinchuan 750021,China
  • 2Ningxia Key Laboratory of Intelligent Sensing for Desert Information,Yinchuan 750021,China
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    References(30)

    [9] ZHANG J B, WANG R W, LIU F L et al. Crack detection on the friction pads of high-speed rail[J]. Journal of Optoelectronics·Laser, 32, 962-969(2021).

    [15] BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4: optimal speed and accuracy of object detection[J/OL]. arXiv, 2004-10934(2020).

    [19] TARG S, ALMEIDA D, LYMAN K. Resnet in resnet: generalizing residual architectures[J/OL]. arXiv, 1603-08029(2016).

    [28] LV H F, LU H C. Research on traffic sign recognition technology based on YOLOv5 algorithm[J]. Journal of Electronic Measurement and Instrumentation, 35, 137-144(2021).

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    Miao-sen ZHOU, Quan-wu TANG, Tian-tian SHI, Tong-lan LUO, Ze-xin ZHANG, Yong-xia XUE. Rail surface crack detection algorithm based on improved YOLOv5s[J]. Chinese Journal of Liquid Crystals and Displays, 2023, 38(5): 666

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    Paper Information

    Category: Research Articles

    Received: Aug. 13, 2022

    Accepted: --

    Published Online: Jul. 4, 2023

    The Author Email: Quan-wu TANG (tangqw@nxu.edu.cn)

    DOI:10.37188/CJLCD.2022-0267

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