With the rapid development of the transportation industry, railway transportation plays a crucial role. Manual inspection methods are time-consuming, labor-intensive, and highly subjective. Therefore, there is an urgent need for a more efficient and accurate flaw detection method. This system is a portable rail flaw detection device based on machine vision, with YOLOv5 as its core deep learning algorithm. The system captures surface images of the rail through a camera and transmits them in real-time to the host computer for analysis. Leveraging the powerful real-time object detection capability of YOLOv5s, the system can accurately identify and locate various types of rail surface damages, such as cracks, fractures, and wear. Compared to traditional manual inspection, this system is more efficient and greatly improves the accuracy and efficiency of rail flaw detection. It has a smaller size and is convenient to carry, making it suitable for working in various environments and conditions, greatly enhancing the practicality and flexibility of the device.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Machine vision-based portable track inspection system


    Beteiligte:
    Zhang, Xiang (Herausgeber:in) / Li, Qing (Autor:in) / Wei, Lijin (Autor:in) / Qu, Xin (Autor:in) / Cheng, Kai (Autor:in) / Chang, Yanbo (Autor:in) / Zhou, HouLe (Autor:in)

    Kongress:

    Intelligent Photonics (Meta) Technology Symposium (IPTS2023) ; 2023 ; Wuhan, China


    Erschienen in:

    Proc. SPIE ; 12785 ; 1278502


    Erscheinungsdatum :

    11.08.2023





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Inertial Measurement System for Track Alignment Inspection Based on Machine Vision

    Peng, Lele / Zhang, Huiling / Li, Xin et al. | TIBKAT | 2021


    Inertial Measurement System for Track Alignment Inspection Based on Machine Vision

    Peng, Lele / Zhang, Huiling / Li, Xin et al. | ASCE | 2020


    Railway track machine vision self-walking inspection equipment

    HE QING / WANG QIHANG / WANG XIAOMING et al. | Europäisches Patentamt | 2022

    Freier Zugriff

    Portable urban road vehicle violation inspection system and method based on machine vision

    GAO QIAN / WANG CHUANYUN / YANG JIANQI et al. | Europäisches Patentamt | 2023

    Freier Zugriff

    Railway track inspection method based on computer vision

    WU SHUANG / LEI HENGXIN | Europäisches Patentamt | 2023

    Freier Zugriff