Dual-Modal Drowsiness Detection to Enhance Driver Safety
作者机构：Faculty of Information Science and TechnologyMultimedia UniversityAyer KerohMelaka75450Malaysia
出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)
年 卷 期：2024年第81卷第12期
页 面：4397-4417页
学科分类：0502[文学-外国语言文学] 050201[文学-英语语言文学] 05[文学]
基 金：funded by the TM R&D grant number RDTC/221046
主 题：Drowsy advanced driver assistance system driver safety on-the-road experiments
摘 要：In the modern world,the increasing prevalence of driving poses a risk to road safety and necessitates the development and implementation of effective monitoring *** study aims to enhance road safety by proposing a dual-modal solution for detecting driver drowsiness,which combines heart rate monitoring and face recognition *** research objectives include developing a non-contact method for detecting driver drowsiness,training and assessing the proposed system using pre-trained machine learning models,and implementing a real-time alert feature to trigger warnings when drowsiness is *** learning models based on convolutional neural networks(CNNs),including ResNet and DenseNet,were trained and *** CNN model emerged as the top performer compared to ResNet50,ResNet152v2,and *** tests,employing different camera angles using Logitech BRIO 4K Ultra HD Pro Stream webcam produces accurate face recognition and heart rate ***-world vehicle tests involved six participants and showcased the system’s stability in calculating heart rates and its ability to correlate lower heart rates with *** incorporation of heart rate and face recognition technologies underscores the effectiveness of the proposed system in enhancing road safety and mitigating the risks associated with drowsy driving.
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