Icicle damage is common in tunnels in cold regions, posing a major threat to the operational safety of railway trains. To address the many drawbaks in existing deicing methods, this paper proposes the design of a tunnel deicing vehicle with an icicle detection and positioning system. Firstly, a tunnel deicing vehicle structure is designed for the tunnel scenario in which the deicing vehicle operates. Moreover, icicle detection experiments are conducted based on the You Only Look Once (YOLO) deep-learning method. In addition, ice positioning experiments are carried out based on the stereo vision, with the relative error within 2.2%, proving the feasibility of the detection and the positioning system. The combination of the deicing vehicle and the binocular vision system enables the detection, localization, and cleaning of the icicles. This deicing process reduces manual intervention, improves the efficiency and quality of deicing operations, and ensures the safety of railway operations.


    Zugriff

    Download

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Stereo Vision–Based Icicle Detection for Tunnel Deicing


    Weitere Titelangaben:

    Transportation Research Record: Journal of the Transportation Research Board


    Beteiligte:
    Dong, Yuezhao (Autor:in) / Du, Maoqiang (Autor:in) / Tian, Hongzhi (Autor:in)


    Erscheinungsdatum :

    04.04.2025




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    ICICLE - Ice Core Integrated Calescent Linear Extractor

    Jagatia, Bhavi / Klapper, Hannah M. / Diaz Artiles, Ana | AIAA | 2018


    Unmanned aerial vehicle for tunnel deicing and deicing method thereof

    XUE ZHENHUA / DONG YANDONG / WANG YIBO et al. | Europäisches Patentamt | 2025

    Freier Zugriff

    MOVALE ICICLE CRUSHING SYSTEM FOR ELECTRIC RAILWAY TUNNELS

    KIM SEONG JONG | Europäisches Patentamt | 2021

    Freier Zugriff

    Assessing GOES-16 Cloud Products using ICICLE Aircraft Observations

    Zhujun Li / William L Smith Jr. / Douglas Spangenberg | NTRS