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Due to the drowsiness of drivers, car accidents kill thousands of people worldwide every year.This fact clearly illustrates the need for a sleep sensor application to help prevent such accidents and ultimately save lives. In this work, we propose a novel intensive learning method based on neutral neural networks (CNN) to deal with this problem. In this project we aim to develop a prototype drowsiness detection system. The system works by monitoring the driver's eyes and ringing the alarm while it is drying.
The system is a real-time control system that is not intrusive. The priority is to improve driver safety without intrusion. In this project, the driver's eyelid is detected. When a driver's eyes are closed for an extended period of time, the driver is considered indifferent, and an alarm rings. The Haar Cascade library is used to detect facial features, and programming is performed in OpenCV.