Pantograph-catenary arcing refers to an abnormal phenomenon occurring in pantograph-catenary system due to poor contact or other factors, which significantly impacts the normal operation of high-speed railway. Therefore, the detection of arcing occurrences holds significant importance for the intelligent maintenance of pantograph-catenary systems. However, the scarcity of arcing data in pantograph-catenary datasets limits the efficacy of supervised learning methods for arcing detection. To address this issue, we propose a novel pantograph-catenary arcing detection model that integrates semantic segmentation with generative adversarial networks. The model first modifies the loss function of the U $^2$   2 -Net network to tailor it specifically for pantograph-catenary semantic segmentation. To generate finer normal pantograph-catenary images, attention mechanism is incorporated into the SPADE-based pantograph-catenary scene generation model. Finally, an improved differencing method is employed to compute the arcing image by subtracting the generated normal pantograph-catenary image from the actual pantograph-catenary image. The experimental results validate the effectiveness of the method for pantograph-catenary arcing detection in the absence of prior arcing knowledge, with a recall rate of 75.3% and an F1-Score of 69.63%. Compared to other advanced pantograph-catenary arcing methods, this method exhibits superior performance.


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    Titel :

    A pantograph-catenary arcing detection model for high-speed railway based on semantic segmentation and generative adversarial network


    Weitere Titelangaben:

    X. LIU ET AL.
    INTERNATIONAL JOURNAL OF RAIL TRANSPORTATION


    Beteiligte:
    Liu, Xiaohong (Autor:in) / Wang, Xiaoyu (Autor:in) / Quan, Wei (Autor:in) / Gu, Guoxin (Autor:in) / Xu, Xiaoqian (Autor:in) / Gao, Shibin (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    04.07.2025


    Format / Umfang :

    22 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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