Deep Learning Approach for Drowsiness Detection Using Facial Features

The suggested system uses the YOLOv5 object detection algorithm to look for signs of tiredness in driver. Using YOLOv5 object detection and facial feature analysis, this software project aims to develop a tiredness detecting system for drivers. Real-time video feeds via a camera installed in the vehicle will be employed by the system to monitor the driver's eye closure movements and mouth opening movements for yawning detection. The YOLOv5 method and the Python programming language will be utilized to create the suggested system. To process images and videos, the OpenCV library will be employed. When the system notices tiredness, it will analyze the patterns of these facial traits and sound an alarm alert to warn the driver.