The Integrated patrolling inspection train has been used worldwide for railway safety monitoring. The camera mounted under the train can capture the track image for abnormal fastener detection. For solving the high false positive alarm of rail fastener recognition arising from ballasts occlusion and non-uniform illumination, we proposed a fastener defect recognition method using deep learning model, and constructed four network structures based on AlexNet and ResNet to learn the fastener feature in complex background. The experimental results show that the RestNet18 network model with unfreezing convolutional layers not only performs well at the trained line, but also has good generalization at the new line, which is a more appropriate model for fastener recognition by comparison with the traditional handcraft feature and existing deep learning models.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Rail fastener automatic recognition method in complex background


    Contributors:
    Wang, Shengchun (author) / Dai, Peng (author) / Du, Xinyu (author) / Gu, Zichen (author) / Ma, Yufeng (author)

    Conference:

    Tenth International Conference on Digital Image Processing (ICDIP 2018) ; 2018 ; Shanghai,China


    Published in:

    Proc. SPIE ; 10806


    Publication date :

    2018-08-09





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Rail fastener

    CORONEL WOLFGANG E | European Patent Office | 2018

    Free access

    RAIL FASTENER

    CORONEL WOLFGANG E | European Patent Office | 2018

    Free access

    Rail joint fastener

    European Patent Office | 2021

    Free access

    Rail joint fastener

    KI JUN LEE / SUNG KI LEE | European Patent Office | 2021

    Free access