• Tanvi Naxane
• Sayli Shrungare
• Shraddha Bhandarkar
• Shivani Rajhance
• Mrs. Pranali Deshmukh, Tushar Kute

The primary purpose of this paper was to propose a way to alert sleepy drivers in the act of driving. Most of the traditional methods to detect drowsiness are based on behavioural aspects while some are intrusive and may distract drivers, while some require expensive sensors/hardware. Therefore, in this paper, driver’s drowsiness detection system is developed and implemented to aid drowsy drivers from falling asleep and to prevent accidents. The system takes images from the device as input. Using these image templates, the trained model starts execution and predicts/classifies whether the face of the person in the image is drowsy or alert. The proposed model is able to achieve accuracy of 99.93% using CNN on trained image dataset.

CNN, Alarm, VGG16, Drowsiness Detection.

"Neural Network Based Detection of Driver’s Drowsiness", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 7, page no.a70-a77, July-2021, Available :http://www.jetir.org/papers/JETIR2107011.pdf

"Neural Network Based Detection of Driver’s Drowsiness", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 7, page no. ppa70-a77, July-2021, Available at : http://www.jetir.org/papers/JETIR2107011.pdf