According to Central Road Research Institute, more than 30% of the road accidents in India are happening because of driver drowsiness especially in the early hours of the day. This is observed in most of the countries which is a serious threat to the society and has to be addressed at the earliest. The ratio of accidents due to driver drowsiness can be reduced if a drowsiness detection mechanism is in continuous active state in the vehicle, which helps drivers to get an alert and go back to a normal driving mode. The proposed approach focuses on building a drowsiness detection mechanism to alert the driver to avoid the catastrophe. In this work, the detection system can identify whether the driver’s eyes were closed or open even in low light or dim light and how much time the eyes were in closed state. Based on the time the system will generate an alert. The proposed system which is built using convolution neural network achieved 89% accuracy in normal light and 78% accuracy in dim light conditions.
Driver Drowsiness Detection Using Convolution Neural Networks
Smart Innovation, Systems and Technologies
2021-07-14
10 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
IEEE | 2020
|Springer Verlag | 2017
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