M.L & A.I BASED DROWSINESS DETECTION OF DRIVER BY USING RASPBERRY PI









Abstract

The number of vehicle accidents is on the rise these days. Drunk driving or feeling tired while driving are two of the most common causes. When performing duties that demand constant concentration, such as driving a car, drowsiness can be harmful. Human safety is the primary concern in vehicle automation. As a result, the number of fatigue-related automobile accidents can be reduced by recognising drowsiness. As a result, the main goal of this work is to create a prototype of a drowsiness and yawn detection system based on Python and the Dlib model. It is a real-time system which will detect the drowsiness among car drivers by capturing image continuously and will give alert the driver whenever they will feel sleepy. The current work's novelty is based on the frequency with which people blink their eyes and yawn. The per closure value of the eye is analyzed for tiredness detection, and if it exceeds a particular amount, the driver is considered sleepy. Similarly, we will examine the yawn value to detect tiredness, and if it exceeds its minimal threshold value, a yawn alarm will be issued.


Modules


Algorithms


Software And Hardware