The swivel clevis (SC) is a vulnerable part of the Overhead Catenary System (OCS). Regular inspection using computer vision technology is an effective way to detect SC defects and improve the OCS operation safety. However, achieving full automation of SC defects detection is still a difficult task due to defective sample scarcity and data distribution shift. To overcome these problems, this paper proposes a novel defects detection method that combines an adaptive SC segmentation network (Adaptive SSN) and local operators. During the inspection process, an unreliability index defined by the model uncertainty and prior knowledge is used to monitor the reliability of the Adaptive SSN. When data distribution shift causes the Adaptive SSN to be unreliable, human annotator will be asked to update the training set and retrain the Adaptive SSN to adapt to the new data distribution. Then the geometric features obtained from segmentation masks and the local features extracted by local operators are used to detect the SC defects. Effectiveness of the proposed method is demonstrated by the experimental results on the data from several high-speed railway lines.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Adaptive Deep Learning for High-Speed Railway Catenary Swivel Clevis Defects Detection


    Contributors:
    Gao, Shibin (author) / Kang, Gaoqiang (author) / Yu, Long (author) / Zhang, Dongkai (author) / Wei, Xiaoguang (author) / Zhan, Dong (author)


    Publication date :

    2022-02-01


    Size :

    4276177 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    THREE PIECE FAILSAFE CLEVIS

    SMEAL ANDREW / TSAI KEVIN | European Patent Office | 2022

    Free access

    THREE-PIECE FAILSAFE CLEVIS

    ANDREW SMEAL / KEVIN TSAI | European Patent Office | 2022

    Free access

    Dual-Eccentric Clevis Joint

    Fairbank, Diana L. / Sertich, John J. | NTRS | 1996


    Clevis sensing lock

    HELSLEY THOMAS J / DALAN LANE DENNIS | European Patent Office | 2019

    Free access