Rail flaw detection is an essential link in the normal operation of the railway. Accurately detecting the internal damage of the rail and repairing the rail in time can find and eliminate the hidden dangers before the accident, and provide a strong security guarantee for the running of the train. In this paper, a rail defect detection method based on improved XGBoost is proposed. The Conditional Generative Adversarial Networks is used to expand the existing rail damage detection data set, then an improved XGBoost model is used to identify and classify the rail flaw detection data. Taking the crack damage type of screw hole as an example, good experimental results are obtained, which further proves the effectiveness and reliability of the scheme.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Rail Defect Detection Method Based on Improved XGBoost


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Liu, Qi (editor) / Liu, Xiaodong (editor) / Cheng, Jieren (editor) / Shen, Tao (editor) / Tian, Yuan (editor) / Zhang, Chongjie (author) / Zhao, Qinjun (author) / Shen, Tao (author) / Sun, Bin (author)

    Conference:

    International Conference on Computer Engineering and Networks ; 2022 ; Haikou, China November 04, 2022 - November 07, 2022



    Publication date :

    2022-10-20


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Urban Rail Transit Passenger Flow Forecasting—XGBoost

    Sun, Xiaoli / Zhu, Caihua / Ma, Chaoqun | ASCE | 2022


    Urban Rail Transit Passenger Flow Forecasting - XGBoost

    Sun, Xiaoli / Zhu, Caihua / Ma, Chaoqun | TIBKAT | 2022


    Rail Fastener Defect Detection of Heavy Haul Railway Based on Improved YOLOv8

    Li, Xinman / Cao, Yuan / Wang, Feng et al. | IEEE | 2024


    Vehicle-mounted steel rail defect detection method

    ZHANG HUI / XU CHEN / FAN GUOPENG et al. | European Patent Office | 2022

    Free access

    Filter-based feature selection for rail defect detection

    Mandriota, C. / Nitti, M. / Ancona, N. et al. | British Library Online Contents | 2004