Chuanhui Lau, Hungyang Leong, Joonhuang Chuah, N. Kamarudin
{"title":"An Eye Fatigue Recognition System using YOLOv2","authors":"Chuanhui Lau, Hungyang Leong, Joonhuang Chuah, N. Kamarudin","doi":"10.1109/i-PACT52855.2021.9696747","DOIUrl":null,"url":null,"abstract":"The rapid increase in global population significantly drives the hiking demand for transportations. This trend further leads to the increase in the number of road traffic accidents globally. Based on a study, fatigue due to prolonged driving is one of the leading causes for traffic accidents. With a customized Graphical User Interface (GUI), this work aims to develop an eye fatigue recognition system using YOLOv2 model. The proposed method used PERCLOS and blink rate parameters as indicators to determine the alertness of the user. This proposed method achieved a real-time average accuracy of 99.23% in normal lighting conditions and 98.57% in low light conditions.","PeriodicalId":335956,"journal":{"name":"2021 Innovations in Power and Advanced Computing Technologies (i-PACT)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 Innovations in Power and Advanced Computing Technologies (i-PACT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/i-PACT52855.2021.9696747","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The rapid increase in global population significantly drives the hiking demand for transportations. This trend further leads to the increase in the number of road traffic accidents globally. Based on a study, fatigue due to prolonged driving is one of the leading causes for traffic accidents. With a customized Graphical User Interface (GUI), this work aims to develop an eye fatigue recognition system using YOLOv2 model. The proposed method used PERCLOS and blink rate parameters as indicators to determine the alertness of the user. This proposed method achieved a real-time average accuracy of 99.23% in normal lighting conditions and 98.57% in low light conditions.
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