Detection of Driver Drowsiness Based on Eye and Mouth Movements Using Convolutional Neural Networks

Budiarianto Suyo Kusumo, Siwi Oktaviana, W. Sulandari, A. Heryana, R. S. Yuwana, Endang Suryawati, A. R. Yuliani, H. Pardede
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Abstract

The Increasing of road mobility triggers the increasing of the number of traffic accidents. One of the main factors of the accidents is human errors which are heavily influenced by the driver conditions. Fatigue, drowsiness, and loss of concentration are among the common driver conditions that could cause traffic accident in addition to high-speed driving behavior. This could be minimized if early warning systems of driver conditions existed. This research aims to develop an early detection system for driver conditions using Convolutional Neural Network (CNN) method. Here, we investigate the effect of the depth of CNN and other hyper-parameters and observe their performance. We used eye movements and mouth conditions to be an indicator driver conditions. We evaluate the method using public dataset that contains image data of drivers on the highway in a state of yawning, not yawning, eyes open, and eyes closed. The experiment showed the best parameters with a learning rate of 0.001 and an epoch of 100. The resulting accuracy reached 99.31%.
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基于口眼运动的卷积神经网络驾驶员睡意检测
道路机动性的增加引发了交通事故数量的增加。人为失误是造成交通事故的主要原因之一,而人为失误在很大程度上受驾驶员状况的影响。疲劳、困倦和注意力不集中是除高速驾驶行为外,可能导致交通事故的常见驾驶员状况。如果有驾驶员状况的早期预警系统,这种情况可以最小化。本次研究的目的是利用卷积神经网络(CNN)方法开发驾驶员状态的早期检测系统。在这里,我们研究了CNN深度和其他超参数的影响,并观察了它们的性能。我们用眼动和嘴的情况作为指示驱动条件。我们使用公共数据集来评估该方法,该数据集包含高速公路上驾驶员在打哈欠、不打哈欠、睁眼和闭眼状态下的图像数据。实验表明,最佳参数为学习率为0.001,历元为100。结果准确率达到99.31%。
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