Traffic operation factors include vehicle and cargo damage, environmental effects, access for emergency vehicles, transit routes, and traffic speeds and volumes. In India, traffic rule violations and high-speed vehicles contribute to a large number of accidents and fatalities on the road. To mitigate this issue, the government has implemented speed breakers as a safety measure. In most cases, speed humps are only advised for use on streets with a speed restriction of 30 mph (50 km/h) or less. However, many speed breakers lack proper signboards, and drivers often do not see them, leading to accidents. In light of this, a speed breaker detection system using deep learning is relevant and necessary in the current scenario. This research proposes a system that uses deep learning image detection algorithms to detect speed breakers in real-time, thereby increasing their visibility and reducing the risk of accidents. The system is trained using an Indian dataset and will be tested in various driving scenarios to evaluate its performance. The results are expected to show that the proposed system will outperform existing methods and have the potential to significantly improve road safety in India.


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    Titel :

    Deep Learning-Based Speed Breaker Detection


    Weitere Titelangaben:

    SN COMPUT. SCI.


    Beteiligte:
    VT, Mohamed Anas (Autor:in) / Omar, Mohd (Autor:in) / Ahamad, Jameel (Autor:in) / Ahmad, Khaleel (Autor:in) / Khan, Mohd Anas (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    22.05.2024




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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