驾驶员困倦检测的深度学习方法

IF 1.8 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Safety Pub Date : 2023-09-13 DOI:10.3390/safety9030065
Mohammed Imran Basheer Ahmed, Halah Alabdulkarem, Fatimah Alomair, Dana Aldossary, Manar Alahmari, Munira Alhumaidan, Shoog Alrassan, Atta Rahman, Mustafa Youldash, Gohar Zaman
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引用次数: 0

摘要

疲劳驾驶是交通事故的普遍原因,尤其是在高速公路上。为了能够立即采取补救措施来发现司机的睡意并加强道路安全,寻求了解情况已成为一项必不可少的任务。为了解决道路安全问题,该模型提供了一种使用卷积神经网络(CNN)评估驾驶员疲劳程度的方法,该方法基于驾驶员眼球运动的变化。进一步,借助CNN和VGG16模型检测面部困倦表情,并将其分为四类(开、闭、打呵欠、不打呵欠)。随后,使用2900张与驾驶员困倦相关的眼睛状况图像数据集来测试这些模型,其中包括性别、年龄、头部位置和照明等不同范围的特征。下放模型的结果显示出高度的问责性,而CNN模型的准确率为97%,精度为99%,召回率和F-score值为99%。VGG16模型的准确率达到74%。这是文献中针对类似问题的最先进的方法之间的相当大的对比。
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A Deep-Learning Approach to Driver Drowsiness Detection
Drowsy driving is a widespread cause of traffic accidents, especially on highways. It has become an essential task to seek an understanding of the situation in order to be able to take immediate remedial actions to detect driver drowsiness and enhance road safety. To address the issue of road safety, the proposed model offers a method for evaluating the level of driver fatigue based on changes in a driver’s eyeball movement using a convolutional neural network (CNN). Further, with the help of CNN and VGG16 models, facial sleepiness expressions were detected and classified into four categories (open, closed, yawning, and no yawning). Subsequently, a dataset of 2900 images of eye conditions associated with driver sleepiness was used to test the models, which include a different range of features such as gender, age, head position, and illumination. The results of the devolved models show a high degree of accountability, whereas the CNN model achieved an accuracy rate of 97%, a precision of 99%, and recall and F-score values of 99%. The VGG16 model reached an accuracy rate of 74%. This is a considerable contrast between the state-of-the-art methods in the literature for similar problems.
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来源期刊
Safety
Safety Social Sciences-Safety Research
CiteScore
3.20
自引率
5.30%
发文量
71
审稿时长
7 weeks
期刊最新文献
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