Drowsy driving is one of the reasons for automobile accidents. We propose a ‘Driver Drowsiness Detection System’ which can help to reduce automobile accidents caused due to drowsy driving. We propose a Convolutional Neural Network (CNN) model that is capable of detecting drowsiness based on closing of the eyelids of the driver, and a future scope of a cost effective and low power consuming stand alone system that can be installed inside the vehicle, which basically consists of a Convolutional Neural Network (CNN) model interfaced with a Raspberry Pi microcontroller and a webcam to capture facial images of the driver. Based on the time duration for which the eyes are closed, a score is calculated. When this score crosses a predetermined threshold, it prompts the software to play a beeping alarm and alert the driver. The score remains zero for the duration when the eyes remain open. When integrated with a Raspberry Pi and powered with the vehicle’s battery, the system can easily be placed inside a vehicle and can act as a constant monitor for a driver.


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    Titel :

    Driver Drowsiness Detection Using Deep Learning


    Beteiligte:
    Pawar, Rupali (Autor:in) / Wamburkar, Saloni (Autor:in) / Deshmukh, Rutuja (Autor:in) / Awalkar, Nikita (Autor:in)


    Erscheinungsdatum :

    01.10.2021


    Format / Umfang :

    1190369 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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