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

作　　者：Abdulwahab Ali Almazroi Mohammed A.Alqarni Nida Aslam Rizwan Ali Shah 

机构地区：[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

出　　处：《Intelligent Automation & Soft Computing》2023年第8期2153-2174,共22页智能自动化与软计算（英文）

基　　金：Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through Project Number MoE-IF-UJ-22-4100409-1.

摘　　要：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.

关 键 词：Deep learning convolutional neural network Tensorflow drowsiness and yawn detection seat belt detection object detection 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]