Drowsiness Detection System Using Machine Learning

Abstract

Drowsy driving is a serious safety concern that affects millions of people worldwide. to prevent accidents caused by drowsy driving, a drowsiness detection system using ML is proposed in this project. The system uses computer vision and machine learning algorithms to detect signs of drowsiness in drivers and alert them while driving. According to various studies, the number of accidents caused by the drowsiness of drivers is much higher than the number of accidents caused by drunk driving. Most accidents are caused bydrowsiness and it can be reduced by having a system that can detect yawning and eye closure, and alert the driver to prevent major injuries. The system uses a camera to capture video of the driver's face and eyes. Then the captured video is processed using libraries of Python such as OpenCVto detect facial landmarks, eye blinks, and head movements. Machine learning algorithms are then used to analyze these features and determine whether the driver is drowsy or alert. The system gives real-time alerts to the drivers when signs of drowsiness are found. The driver can also set their own preferences for the alert based on their personal preferences as a sound or flash on their face. In conclusion, this project proposes a drowsiness detection system using Python that is accurate, reliable, and easy to use. The system can reduce the number of accidents caused by drowsy driving and improve road safety.