Open Access

ARTICLE

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

by Abdulwahab Ali Almazroi^1,*, Mohammed A. Alqarni², Nida Aslam³, Rizwan Ali Shah⁴

1 University of Jeddah, College of Computing and Information Technology at Khulais, Department of Information Technology, Jeddah, Saudi Arabia
2 University of Jeddah, College of Computer Science and Engineering, Department of Software Engineering, Jeddah, Saudi Arabia
3 Department of Computer Science, National College of Business Administration & Economics, Bahawalpur Campus, 63100, Pakistan
4 Department of Computer Science and Information Technology, The Islamia University of Bahawalpur, Rahim Yar Khan Campus, Punjab, 64200, Pakistan

* Corresponding Author: Abdulwahab Ali Almazroi. Email:

(This article belongs to the Special Issue: Computer Vision and Machine Learning for Real-Time Applications)

Intelligent Automation & Soft Computing 2023, 37(2), 2153-2174. https://doi.org/10.32604/iasc.2023.039732

Received 13 February 2023; Accepted 23 April 2023; Issue published 21 June 2023

Abstract

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.

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Keywords

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