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A Real-time Driver Fatigue Detection Method Based on Two-Stage Convolutional Neural Network ?

机译：基于两阶段卷积神经网络的实时驱动疲劳检测方法 ？

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摘要

Fatigue-related traffic accidents have a higher mortality rate and cause more significant damage to the environment. To ensure driving safety, a real-time driver fatigue detection method based on convolutional neural network (CNN) is proposed in this paper. The proposed fatigue driving detection method is cascaded by two CNN-based stages, including a detecting phase and classifying phase. The Location Detection Network is designed to extract facial features and localize the driver’s eyes and mouth regions. Then the State Recognition Network is training to recognize the driver’s eyes and mouth status. Simulations show that the proposed method has good effect of real time process and high accuracy of detection. Experiments conducted on Raspberry Pi 4 embedded system indicate that the proposed method has a good performance in the real driving environment.
机译：疲劳相关的交通事故具有更高的死亡率，对环境造成更大的重大损害。为确保驾驶安全性，本文提出了一种基于卷积神经网络（CNN）的实时驱动疲劳检测方法。所提出的疲劳驱动检测方法通过两个基于CNN的阶段级联，包括检测相和分类阶段。位置检测网络旨在提取面部特征，并本地化驾驶员的眼睛和口腔区域。然后，国家识别网络正在培训，以识别驾驶员的眼睛和嘴部状态。仿真表明，该方法具有实时过程的良好效果和高精度的检测。在覆盆子PI 4嵌入式系统上进行的实验表明该方法在真正的驾驶环境中具有良好的性能。

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