{"title":"使用迁移学习的实时驾驶员困倦检测","authors":"N. Gupta, Faizan Khan, Bhavna Saini","doi":"10.1109/ICCT56969.2023.10075913","DOIUrl":null,"url":null,"abstract":"According to statistics, drowsy driving is the leading cause of accidents worldwide that result in the loss of precious lives and worsen public health. When a driver is fatigued, cameras can be employed to detect their drowsiness and inform them well before which can help in decreasing accidents. This work employes a transfer Learning model DenseNet to identify the driver drowsiness in real time. The MRL eye dataset of 84923 images has been used and the model works well with 91.56% accuracy.","PeriodicalId":128100,"journal":{"name":"2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Real Time Driver Drowsiness Detecion using Transfer learning\",\"authors\":\"N. Gupta, Faizan Khan, Bhavna Saini\",\"doi\":\"10.1109/ICCT56969.2023.10075913\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"According to statistics, drowsy driving is the leading cause of accidents worldwide that result in the loss of precious lives and worsen public health. When a driver is fatigued, cameras can be employed to detect their drowsiness and inform them well before which can help in decreasing accidents. This work employes a transfer Learning model DenseNet to identify the driver drowsiness in real time. The MRL eye dataset of 84923 images has been used and the model works well with 91.56% accuracy.\",\"PeriodicalId\":128100,\"journal\":{\"name\":\"2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)\",\"volume\":\"30 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-01-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCT56969.2023.10075913\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCT56969.2023.10075913","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Real Time Driver Drowsiness Detecion using Transfer learning
According to statistics, drowsy driving is the leading cause of accidents worldwide that result in the loss of precious lives and worsen public health. When a driver is fatigued, cameras can be employed to detect their drowsiness and inform them well before which can help in decreasing accidents. This work employes a transfer Learning model DenseNet to identify the driver drowsiness in real time. The MRL eye dataset of 84923 images has been used and the model works well with 91.56% accuracy.
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