Jonathan Flores-Monroy, M. Nakano-Miyatake, Enrique Escamilla-Hernández, Hector Perez-Meana
{"title":"司机困倦和分心的检测及其在移动设备上的实现","authors":"Jonathan Flores-Monroy, M. Nakano-Miyatake, Enrique Escamilla-Hernández, Hector Perez-Meana","doi":"10.4067/s0718-07642023000400001","DOIUrl":null,"url":null,"abstract":"This study proposes a system that applies artificial intelligence tools to reduce the number of vehicular accidents produced by driver drowsiness and distraction. The proposed system, which is suitable for any kind of vehicle, detects the driver’s face, which is fed into a deep neural network that analyzes it. The network output is then fed into a detection stage which determines whether the driver is drowsy or distracted, activating an alarm. The proposed system has a low computational complexity, allowing real time implementation on mobile devices. The experimental results show that the proposed system can detect drowsiness and distraction with an accuracy of 95.8% in high performance computers. It also shows an 84.5% accuracy on mobile devices with limited capacity, keeping an acceptable operational speed for its implementation in real time. It is concluded that the proposed system detects driver drowsiness and distraction with high accuracy.","PeriodicalId":35948,"journal":{"name":"Informacion Tecnologica","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Detección de somnolencia y distracción en conductores y su implementación en dispositivos móviles\",\"authors\":\"Jonathan Flores-Monroy, M. Nakano-Miyatake, Enrique Escamilla-Hernández, Hector Perez-Meana\",\"doi\":\"10.4067/s0718-07642023000400001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study proposes a system that applies artificial intelligence tools to reduce the number of vehicular accidents produced by driver drowsiness and distraction. The proposed system, which is suitable for any kind of vehicle, detects the driver’s face, which is fed into a deep neural network that analyzes it. The network output is then fed into a detection stage which determines whether the driver is drowsy or distracted, activating an alarm. The proposed system has a low computational complexity, allowing real time implementation on mobile devices. The experimental results show that the proposed system can detect drowsiness and distraction with an accuracy of 95.8% in high performance computers. It also shows an 84.5% accuracy on mobile devices with limited capacity, keeping an acceptable operational speed for its implementation in real time. It is concluded that the proposed system detects driver drowsiness and distraction with high accuracy.\",\"PeriodicalId\":35948,\"journal\":{\"name\":\"Informacion Tecnologica\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Informacion Tecnologica\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4067/s0718-07642023000400001\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Multidisciplinary\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Informacion Tecnologica","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4067/s0718-07642023000400001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Multidisciplinary","Score":null,"Total":0}
Detección de somnolencia y distracción en conductores y su implementación en dispositivos móviles
This study proposes a system that applies artificial intelligence tools to reduce the number of vehicular accidents produced by driver drowsiness and distraction. The proposed system, which is suitable for any kind of vehicle, detects the driver’s face, which is fed into a deep neural network that analyzes it. The network output is then fed into a detection stage which determines whether the driver is drowsy or distracted, activating an alarm. The proposed system has a low computational complexity, allowing real time implementation on mobile devices. The experimental results show that the proposed system can detect drowsiness and distraction with an accuracy of 95.8% in high performance computers. It also shows an 84.5% accuracy on mobile devices with limited capacity, keeping an acceptable operational speed for its implementation in real time. It is concluded that the proposed system detects driver drowsiness and distraction with high accuracy.
期刊介绍:
The Información tecnológica magazine is a service of the Center for Information Technology (CIT), this service is restricted and prohibited their sale to third parties as well as the total or partial reproduction for commercial purposes. The articles presented in this magazine are for original papers sent by the authors and have been accepted for publication by a committee, and an Editorial Committee of Referees. The Center for Information Technology is not responsible for the opinions contained in the articles, that responsibility rests with the perpetrators of these.