Drive-Awake: A YOLOv3 Machine Vision Inference Approach of Eyes Closure for Drowsy Driving Detection

Jonel R. Macalisang, A. Alon, Moises F. Jardiniano, Deanne Cameren P. Evangelista, Julius C. Castro, Meriam L. Tria
{"title":"Drive-Awake: A YOLOv3 Machine Vision Inference Approach of Eyes Closure for Drowsy Driving Detection","authors":"Jonel R. Macalisang, A. Alon, Moises F. Jardiniano, Deanne Cameren P. Evangelista, Julius C. Castro, Meriam L. Tria","doi":"10.1109/IICAIET51634.2021.9573811","DOIUrl":null,"url":null,"abstract":"Nowadays, road accidents have become a major concern. The drowsiness of drivers owing to overfatigue or tiredness, driving while intoxicated, or driving too quickly is some of the primary causes of this. Drowsy driving contributes to or increases the number of traffic accidents each year. The study presented a technique for detecting driver drowsiness in response to this issue. The sleep states of the drivers in the driving environment were detected using a deep learning approach. To assess if the eyes of particular constant face images of drivers are closed, a convolutional neural network (CNN) model has been developed. The suggested model has a wide range of possible applications, including human-computer interface design, facial expression detection, and determining driver tiredness and drowsiness. The YOLOv3 algorithm, as well as additional tools like Pascal VOC and LabelImg, were used to build this approach, which collects and trains a driver dataset that feels drowsy. The study's total detection accuracy was 100%, with detection per frame accuracy ranging from 49% to 89%.","PeriodicalId":234229,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IICAIET51634.2021.9573811","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12

Abstract

Nowadays, road accidents have become a major concern. The drowsiness of drivers owing to overfatigue or tiredness, driving while intoxicated, or driving too quickly is some of the primary causes of this. Drowsy driving contributes to or increases the number of traffic accidents each year. The study presented a technique for detecting driver drowsiness in response to this issue. The sleep states of the drivers in the driving environment were detected using a deep learning approach. To assess if the eyes of particular constant face images of drivers are closed, a convolutional neural network (CNN) model has been developed. The suggested model has a wide range of possible applications, including human-computer interface design, facial expression detection, and determining driver tiredness and drowsiness. The YOLOv3 algorithm, as well as additional tools like Pascal VOC and LabelImg, were used to build this approach, which collects and trains a driver dataset that feels drowsy. The study's total detection accuracy was 100%, with detection per frame accuracy ranging from 49% to 89%.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
驾驶觉醒:一种YOLOv3闭眼机器视觉推理方法用于疲劳驾驶检测
如今,交通事故已成为一个主要问题。驾驶员因过度疲劳或疲劳而昏昏欲睡、醉酒驾驶或超速驾驶是造成这种情况的一些主要原因。疲劳驾驶导致或增加了每年的交通事故数量。针对这一问题,该研究提出了一种检测驾驶员困倦的技术。使用深度学习方法检测驾驶环境中驾驶员的睡眠状态。为了评估驾驶员的特定恒定面部图像是否闭着眼睛,开发了卷积神经网络(CNN)模型。该模型具有广泛的应用前景,包括人机界面设计、面部表情检测以及驾驶员疲劳和困倦的判断。YOLOv3算法以及Pascal VOC和LabelImg等附加工具被用于构建这种方法,该方法收集并训练让人感觉昏昏欲睡的驾驶员数据集。该研究的总检测精度为100%,每帧检测精度从49%到89%不等。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Text Analytics on Twitter Text-based Public Sentiment for Covid-19 Vaccine: A Machine Learning Approach Eye-Tank: Monitoring and Predicting Water and pH Level in Smart Farming Particle Swarm Optimization for Tuning Power System Stabilizer towards Transient Stability Improvement in Power System Network Multi-Scale Texture Analysis For Finger Vein Anti-Spoofing Utilization of Response Surface Methodology and Regression Model in Optimizing Bioretention Performance
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1