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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Rail fastener automatic recognition method in complex background


    Beteiligte:
    Wang, Shengchun (Autor:in) / Dai, Peng (Autor:in) / Du, Xinyu (Autor:in) / Gu, Zichen (Autor:in) / Ma, Yufeng (Autor:in)

    Kongress:

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


    Erschienen in:

    Proc. SPIE ; 10806


    Erscheinungsdatum :

    09.08.2018





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Rail fastener

    CORONEL WOLFGANG E | Europäisches Patentamt | 2018

    Freier Zugriff

    RAIL FASTENER

    CORONEL WOLFGANG E | Europäisches Patentamt | 2018

    Freier Zugriff

    Rail joint fastener

    Europäisches Patentamt | 2021

    Freier Zugriff

    Rail joint fastener

    KI JUN LEE / SUNG KI LEE | Europäisches Patentamt | 2021

    Freier Zugriff