Real-Time CNN-Based Driver Distraction & Drowsiness Detection System

Nowadays days, the chief grounds of automobile accidents are driver fatigue and distractions. With the development of computer vision technology, a cutting-edge system has the potential to spot driver distractions or sleepiness and alert them, reducing accidents. This paper presents a novel approach to detecting driver tiredness based on eye and mouth movements and object identification that causes a distraction while operating a motor vehicle. Employing the facial landmarks that the camera picks up and sends to classify using a Convolutional Neural Network (CNN) any changes by focusing on the eyes and mouth zone, precision is achieved. One of the tasks that must be performed in the transit system is seat belt detection to lessen accidents caused by sudden stops or high-speed collisions with other cars. A method is put forth to use convolution neural networks to determine whether the motorist is wearing a seat belt when a driver is sleepy, preoccupied, or not wearing their seat belt, this system alerts them with an alarm, and if they don't wake up by a predetermined time of 3 s threshold, an automatic message is sent to law enforcement agencies. The suggested CNN-based model exhibits greater accuracy with 97%. It can be utilized to develop a system that detects driver attention or sleeps in real-time.