Currently, image detection based on deep learning has a good application prospect for railroad fault diagnosis. However, the existing methods of applying deep learning to fastener fault detection more or less have some drawbacks, such as computational complexity, slow detection speed, low detection accuracy, poor robustness, etc. To address these issues, this paper introduces a fastener fault identification method for railroad lines based on CenterNet. By introducing the Convolutional Block Attention Module and the jump connection mechanism, and conducting comparative experiments on different networks, it is proved that the network proposed in this paper has a better speed and accuracy for fastener detection.


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    Titel :

    Research on fault identification method of railroad fastener based on center point prediction network


    Beteiligte:
    Lei, Tao (Herausgeber:in) / Zhang, Dehai (Herausgeber:in) / Chen, Zihao (Autor:in) / Han, Zhen (Autor:in)

    Kongress:

    Sixth International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024) ; 2024 ; Wuhan, China


    Erschienen in:

    Proc. SPIE ; 13275


    Erscheinungsdatum :

    27.09.2024





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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