Driver Drowsiness Detection Based On the DenseNet 201 Model - Alaa Abdulraheem Yasir, Dr. Mustafa J. Hayawi, Dr. Ali Hussein Hasan, (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
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.