In order to ensure the safety of rail transit, detecting the flaws on the rail surface is vitally important. Instead of present manual inspections, detecting defects on rail surface by an automatic approach enables the work more efficient and safe currently. In this paper, we propose a novel two-stage pipeline method for defect detection on rail surface by localizing rails and sliding a deep convolutional neural network (DCNN) on rail surface. Specifically, in the first stage, we use an anchor-free detector to locate the tracks in original images and get the cropped images which focus on rail part. In the second stage, a trained deep convolutional neural network slide on the cropped images to detect defects and we can finally get the types and approximate locations of the defects on rail surface. The experimental results show that the proposed method has robustness and achieves practical performance in defect detection precision.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Rail surface defect detection based on deep learning


    Contributors:
    Li, Xiaoqing (author) / Zhou, Ying (author) / Chen, Hu (author)

    Conference:

    Eleventh International Conference on Graphics and Image Processing (ICGIP 2019) ; 2019 ; Hangzhou,China


    Published in:

    Proc. SPIE ; 11373


    Publication date :

    2020-01-03





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Research on deep learning method for rail surface defect detection

    Feng, Jiang Hua / Yuan, Hao / Hu, Yun Qing et al. | IET | 2020

    Free access

    Research on deep learning method for rail surface defect detection

    Feng, Jiang Hua / Yuan, Hao / Hu, Yun Qing et al. | Wiley | 2020

    Free access

    Rail surface anomaly detection based on deep learning

    Shi, Lei / Wu, Junjie / Sun, Yongkui | British Library Conference Proceedings | 2023


    Rail surface anomaly detection based on deep learning

    Shi, Lei / Wu, Junjie / Sun, Yongkui | SPIE | 2023


    Information collection unmanned aerial vehicle for steel rail surface defect detection based on deep learning network

    YONESO MAKOTO / CONG CHAO / JU BENXIANG et al. | European Patent Office | 2022

    Free access