Detecting sleepiness in drivers while driving is essential in order to prevent accidents and reduce the number of mortality caused by drivers sleeping behind the wheels. With the rapid growth of the population, the number of accidents on the roads has increased dramatically, and many people have died as a result of those accidents. It’s a serious issue that needs to be addressed in order to reduce accidents. According to research conducted by the National Sleep Foundation, roughly 20% of drivers experience drowsiness while driving. These figures paint a terrible picture. This paper discusses a method for reducing accidents due to driver drowsiness. The VGG-16 architecture is used for detecting driver drowsiness and achieved 96.79% accuracy with 0.08 loss.


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    Titel :

    Driver Drowsiness Detection for Road Safety Using Deep Learning


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Muthusamy, Hariharan (Herausgeber:in) / Botzheim, János (Herausgeber:in) / Nayak, Richi (Herausgeber:in) / Saini, Parul (Autor:in) / Kumar, Krishan (Autor:in) / Kashid, Shamal (Autor:in) / Negi, Alok (Autor:in) / Saini, Ashray (Autor:in)


    Erscheinungsdatum :

    2023-05-26


    Format / Umfang :

    9 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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