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Design of High Speed Railway Turnout Structural Damage Identification System Based on Machine Learning

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Advanced Hybrid Information Processing (ADHIP 2019)

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Abstract

In order to improve the damage detection and identification ability of high-speed railway turnout structure, a machine learning-based damage identification method for high-speed railway turnout structure is proposed, and the computer vision image analysis method is used to detect the damage of high-speed railway turnout structure. The super-linear segmentation and feature recognition of the damaged parts of high-speed railway turnout structures are realized by means of active contour detection, and the feature segmentation and localization of high-speed railway turnout structures are carried out in the damaged areas. According to the result of feature matching, the machine learning algorithm is used to identify the damage of high-speed railway turnout structure. The simulation results show that the accuracy of the proposed method for damage identification of high-speed railway turnout structure is high, and the ability of damage detection and identification of high-speed railway turnout structure is stronger than that of high-speed railway turnout structure.

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Authors and Affiliations

  1. Railway Engineering College, Wuhan Railway Vocational College of Technology, Wuhan, 430205, China

    Ailin Wang

Authors
  1. Ailin Wang
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Correspondence to Ailin Wang .

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Editors and Affiliations

  1. Nanjing University, Nanjing, China

    Guan Gui

  2. Harbin Engineering University, Harbin, China

    Lin Yun

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Wang, A. (2019). Design of High Speed Railway Turnout Structural Damage Identification System Based on Machine Learning. In: Gui, G., Yun, L. (eds) Advanced Hybrid Information Processing. ADHIP 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 302. Springer, Cham. https://doi.org/10.1007/978-3-030-36405-2_16

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  • DOI: https://doi.org/10.1007/978-3-030-36405-2_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36404-5

  • Online ISBN: 978-3-030-36405-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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