To detect the wheelset deformation and wear, an intuitive method is to first collect wheelset multi-line laser stripe images by monitors, and then extract the centerlines to construct 3D contours that are transferred to the cloud data center by 6G communication. The images, however, contain flairs and fractures due to the influence of environmental interference and reflected light on the smooth surfaces. The image defects affect the accurate extraction of stripe centerlines. To segment the defects and inpaint them, we propose a new lightweight U-shaped segmentation model URS. A target-shaped receptive field is designed to efficiently extract the details of the local, the ring-shaped, and the cross-shaped context around the local, which facilitates segmenting various defects. A scale-select sub-module is designed to adjust the weights of features from different receptive fields. To train the model, a multi-line laser image defect segmentation dataset MLIDSD is constructed. Experiments demonstrate that the performance of our model surpasses twelve SOTA models explicitly and can achieve a balance between the accuracy and the lightweight requirement.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    URS: A Light-Weight Segmentation Model for Train Wheelset Monitoring


    Contributors:
    Guo, Xiaoxuan (author) / Ji, Zhenyan (author) / Feng, Qibo (author) / Wang, Huihui (author) / Yang, Yanyan (author) / Li, Zhao (author)


    Publication date :

    2023-07-01


    Size :

    2849180 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Train wheelset size measuring box

    HUANG LEI | European Patent Office | 2021

    Free access

    Reducing wheelset noice levels on modern train

    Demilly, Françoi / Pignerol, Christian | IuD Bahn | 2006


    Influence of wheelset rotational motion on train collision response and wheelset lift mechanism

    Zhang, Jingke / Zhu, Tao / Yang, Bing et al. | Taylor & Francis Verlag | 2023