Driver Fatigue Detection Based on Convolutional Neural Networks Using EM-CNN

With a focus on fatigue driving detection research, a fully automated driver fatigue status detection algorithm using driving images is proposed. In the proposed algorithm, the multitask cascaded convolutional network (MTCNN) architecture is employed in face detection and feature point location, and...

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Bibliographic Details
Published in:Computational intelligence and neuroscience 2020-11, Vol.2020 (2020), p.1-11
Main Authors: Xu, Yi, Yan, Hualin, Zhang, Lan, Zhou, Nana, Zhao, Zuopeng, Zhang, Zhongxin
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Discriminant analysis
Driver fatigue
Eyelid
Face recognition
Fatigue
Feature extraction
Mouth
Neural networks
Physiology
Vision systems
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Summary:With a focus on fatigue driving detection research, a fully automated driver fatigue status detection algorithm using driving images is proposed. In the proposed algorithm, the multitask cascaded convolutional network (MTCNN) architecture is employed in face detection and feature point location, and the region of interest (ROI) is extracted using feature points. A convolutional neural network, named EM-CNN, is proposed to detect the states of the eyes and mouth from the ROI images. The percentage of eyelid closure over the pupil over time (PERCLOS) and mouth opening degree (POM) are two parameters used for fatigue detection. Experimental results demonstrate that the proposed EM-CNN can efficiently detect driver fatigue status using driving images. The proposed algorithm EM-CNN outperforms other CNN-based methods, i.e., AlexNet, VGG-16, GoogLeNet, and ResNet50, showing accuracy and sensitivity rates of 93.623% and 93.643%, respectively.
ISSN:1687-5265
1687-5273
DOI:10.1155/2020/7251280