Many individuals lose their lives in traffic accidents. Sleepy driving is one of the leading causes to road accidents and mortality. Frequently, fatigue and micro nap at the wheel are the underlying causes of major accidents. Before a dangerous scenario arises, early symptoms of drowsiness can be identified. The majority of conventional approaches for detecting sleepiness are based on behavioural factors. So, a light weight real time drowsiness detection model employing Deep Learning algorithms are used to detect the drowsiness of the drivers. Based on adaptive threshold technique, the system detects the sleepiness of the driver by identifying facial landmarks. This model can be mounted in Raspberry Pi for the real time monitoring. Based on the threshold value of the output, the buzzer warning will be given to the drivers to detect the drowsy state of the driver.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Driver Drowsiness Detection using Deep Learning


    Contributors:


    Publication date :

    2022-03-29


    Size :

    1327093 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Driver Drowsiness Detection Using Deep Learning

    Ojha, Dhiren / Pawar, Amit / Kasliwal, Gaurav et al. | IEEE | 2023


    Driver Drowsiness Detection Using Deep Learning

    Varshitha, V. Sree / Iqbal, M. Mohamed / Amrutha, V. et al. | IEEE | 2023


    Driver Drowsiness Detection Using Deep Learning

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


    Enhanced Driver Drowsiness Detection using Deep Learning

    Singh Dipender / Singh Avtar | DOAJ | 2023

    Free access

    Deep Learning based Driver Drowsiness Detection

    Patel, Parth P. / Pavesha, Chirag L. / Sabat, Santoshi S. et al. | IEEE | 2022