论文标题
驾驶员安全开发实时驱动器嗜睡检测系统基于卷积神经网络
Driver Safety Development Real Time Driver Drowsiness Detection System Based on Convolutional Neural Network
论文作者
论文摘要
本文着重于道路上的驾驶员安全挑战，并提出了一种新型的驾驶员嗜睡检测系统。在该系统中，为了检测驾驶员的睡眠状态下降，作为嗜睡的迹象，卷积神经网络（CNN）用于实时应用的两个目标，包括高准确性和牢固性。引入了三个网络，作为一个潜在的眼睛状态分类网络，其中一个网络是一个完全设计的神经网络（FD-NN），而其他网络则使用额外设计的层（TL-VGG）在VGG16和VGG19中使用转移学习。在眼睛闭合检测区域中，缺乏可用且准确的眼睛数据集。因此，提出了一个新的综合数据集。实验结果表明，眼睛闭合估计的高精度和低计算复杂性以及所提出的框架在嗜睡检测中的能力。
This paper focuses on the challenge of driver safety on the road and presents a novel system for driver drowsiness detection. In this system, to detect the falling sleep state of the driver as the sign of drowsiness, Convolutional Neural Networks (CNN) are used with regarding the two goals of real-time application, including high accuracy and fastness. Three networks introduced as a potential network for eye status classifcation in which one of them is a Fully Designed Neural Network (FD-NN) and others use Transfer Learning in VGG16 and VGG19 with extra designed layers (TL-VGG). Lack of an available and accurate eye dataset strongly feels in the area of eye closure detection. Therefore, a new comprehensive dataset proposed. The experimental results show the high accuracy and low computational complexity of the eye closure estimation and the ability of the proposed framework on drowsiness detection.
