Road transportation is one of the primary mode of transportation, used by people to travel on a daily basis. Since this mode of transportation is cost-effective, it also makes drivers to transport goods and commodities in highways, especially during night time. These factors result in a profound amount of road accidents. Road accidents remains to be a global concern resulting in innumerable fatalities and injuries. Many of these accidents are caused by driver drowsiness, which is a major issue that requires immediate actions. Using machine learning technology, camera can be used to capture and monitor various driver-related parameters including eye movement and mouth movement to predict the drowsiness and yawning state of the driver. Dlib's pre-trained model is used to detect the facial features of the driver. The pre-trained model use CNN algorithm internally to detect the face. The Mouth Aspect Ratio (MAR) and Eye Aspect Ratio (EAR) is calculated and accordingly alerting the driver to prevent road accidents manifold.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Deep Learning Based Smart Alert System for Driver Drowsiness


    Contributors:


    Publication date :

    2023-12-14


    Size :

    535218 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    DDYDAS: Driver Drowsiness, Yawn Detection and Alert System

    Suri, Bhawna / Verma, Moksh / Thapliyal, Kanika et al. | Springer Verlag | 2021



    Driver Drowsiness Detection and Alert System Using Computer Vision

    Kumar, Suresh / Tomar, Chitrangad Singh | IEEE | 2025


    Guardian Alert: A Deep Learning Approach for Driver Drowsiness Detection and Force Sensing Integration

    Thomas, Jitty Tresa / Mathew, Joseph / George, Melwin et al. | IEEE | 2023


    Driver Drowsiness Detection Using Deep Learning

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