Driver Drowsiness Detection Based On the DenseNet 201 Model

Main Article Content

Dr. Ali Hussein Hasan, Alaa Abdulraheem Yasir, Dr. Mustafa J. Hayawi


Driving a vehicle with drowsiness is a very serious and widespread problem in society, because driver drowsiness negatively impacts the response time of the driver and, as a result, when the level of drowsiness increases in the driver, he loses control of his vehicle. He can unexpectedly veer off the lane, colliding with an obstacle or causing a car to overturn.  In this paper, we present a low-cost, non-intrusive, more accurate, and better solution for detecting driver drowsiness in real-time in real-world driving conditions, whenever the drowsiness is detected, the system activates an audible alarm to alert the driver before he falls asleep. In the proposed method, we used the most important facial components that are considered the most effective for sleepiness. We used the Viola-Jones algorithm to detect the driver’s face and eyes area. Then we inserted the resulting image into the deep convolutional neural network (DenseNet 201). To detect driver drowsiness in real-time, the system has been tested and implemented in a real environment. The experimental results showed that the proposed system can detect driver drowsiness with 99% accuracy.


Download data is not yet available.


Metrics Loading ...

Article Details

How to Cite
Alaa Abdulraheem Yasir, Dr. Mustafa J. Hayawi, D. A. H. H. (2021). Driver Drowsiness Detection Based On the DenseNet 201 Model. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(13), 3682–3692.
Research Articles