Drowsy driving is a widespread cause of traffic accidents, especially on highways. It has become an essential task to seek an understanding of the situation in order to be able to take immediate remedial actions to detect driver drowsiness and enhance road safety. To address the issue of road safety, the proposed model offers a method for evaluating the level of driver fatigue based on changes in a driver’s eyeball movement using a convolutional neural network (CNN). Further, with the help of CNN and VGG16 models, facial sleepiness expressions were detected and classified into four categories (open, closed, yawning, and no yawning). Subsequently, a dataset of 2900 images of eye conditions associated with driver sleepiness was used to test the models, which include a different range of features such as gender, age, head position, and illumination. The results of the devolved models show a high degree of accountability, whereas the CNN model achieved an accuracy rate of 97%, a precision of 99%, and recall and F-score values of 99%. The VGG16 model reached an accuracy rate of 74%. This is a considerable contrast between the state-of-the-art methods in the literature for similar problems.


    Access

    Download


    Export, share and cite



    Title :

    A Deep-Learning Approach to Driver Drowsiness Detection




    Publication date :

    2023




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Driver Drowsiness Detection Using Deep Learning

    Pawar, Rupali / Wamburkar, Saloni / Deshmukh, Rutuja et al. | IEEE | 2021


    Driver Drowsiness Detection Using Deep Learning

    Jain, Anuj Kumar / Sharma, Vikrant / Goel, Sandeep et al. | IEEE | 2023


    Driver Drowsiness Detection using Deep Learning

    Nandhini, P.S. / Kuppuswami, S. / Malliga, S. et al. | IEEE | 2022


    Enhanced Driver Drowsiness Detection using Deep Learning

    Singh Dipender / Singh Avtar | DOAJ | 2023

    Free access

    Driver Drowsiness Detection Using MobileNetV2 and Deep Learning

    Wagh, Dhiraj / Hire, Janhavi / Phad, Mayuri et al. | IEEE | 2024