Railroad safety is given top attention in modern transportation networks, with a focus on preventing accidents and preserving passenger safety. Using state-of-the-art deep learning algorithm YOLOv8 and computer vision techniques, this study presents a novel Railway Passenger Safety System (RPSS) that can detect and predict potentially hazardous passenger behaviors near level crossings and track crossings. The RPSS records visual data in real-time from CCTV camera footage and enables continuous railroad environment monitoring. YOLOv8, renowned for its outstanding object identification skills, is utilized to identify various passenger activities, such as illegal crossings, improper proximity to tracks, or other potentially harmful actions. The system may promptly notify authorities and train operators when risky acts are detected, facilitating prompt action to avert mishaps. This study discusses the RPSS’s architecture and design, including model training and data gathering. The system’s effectiveness is evaluated using a huge dataset of passenger activities, and the results demonstrate that it can swiftly and reliably recognize and forecast dangerous postures. The Railway Passenger Safety System, which is the subject of this research, represents a noteworthy advancement in railway safety technology. It provides a proactive approach to enhance passenger safety, reduce accident rates, and augment overall railway safety. YOLOv8 and computer vision in the rail sector could revolutionize safety protocols and increase everyone’s safety when traveling by train.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Detection Of Railway Accident Risk Using Deep Learning Approach


    Beteiligte:
    Arumugam, M. (Autor:in) / Arun, G. (Autor:in) / C, Dhanapal (Autor:in)


    Erscheinungsdatum :

    05.06.2024


    Format / Umfang :

    434990 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Accident Detection Using Deep Learning

    Yadav, Durgesh Kumar / Renu / Ankita et al. | IEEE | 2020


    A Novel Approach for Accident Detection and Localization Using Deep Learning

    Mudgal, Manisha / Punj, Deepika / Pillai, Anuradha | IEEE | 2023


    Acoustic accident detection system through accident sound deep learning

    JOON HYUN KIM | Europäisches Patentamt | 2022

    Freier Zugriff

    Acoustic accident detection system through accident sound deep learning

    KIM KYU HYUN | Europäisches Patentamt | 2022

    Freier Zugriff

    A Deep Learning Approach to the Citywide Traffic Accident Risk Prediction

    Ren, Honglei / Song, You / Wang, Jingwen et al. | IEEE | 2018