Regular inspection of rail valves and engines is an important task to ensure safety and efficiency of railway networks around the globe. Over the past decade, computer vision and pattern recognition based techniques have gained traction for such inspection and defect detection tasks. An automated end-to-end trained system can potentially provide a low-cost, high throughput, and cheap alternative to manual visual inspection of these components. However, such systems require huge amount of defective images for networks to understand complex defects. In this paper, a multi-phase deep learning based technique is proposed to perform accurate fault detection of rail-valves. Our approach uses a two-step method to perform high precision image segmentation of rail-valves resulting in pixel-wise accurate segmentation. Thereafter, a computer vision technique is used to identify faulty valves. We demonstrate that the proposed approach results in improved detection performance when compared to current state-of-the-art techniques used in fault detection.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    FaultNet: Faulty Rail-Valves Detection using Deep Learning and Computer Vision


    Beteiligte:


    Erscheinungsdatum :

    2019-10-01


    Format / Umfang :

    1786013 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Faulty vision and driver

    Darcus, H.D. | Engineering Index Backfile | 1953


    Corrosion detection with computer vision and deep learning

    Matthaiou, A. / Papalambrou, G. / Samuelides, M. S. | TIBKAT | 2022


    Traffic light detection and classification using computer vision and deep learning

    MARYAM MOOSAEI / MADELINE J GOH / VIDYA NARIYAMBUT MURALI et al. | Europäisches Patentamt | 2018

    Freier Zugriff


    Vision-Based Rain Detection Using Deep Learning

    Europäisches Patentamt | 2017

    Freier Zugriff