Blink Fatigue Detection Algorithm Based on Improved Lenet-5

DOI

10.2991/pntim-19.2019.67How to use a DOI?

Keywords

Fatigue Driving; Blinking Algorithm; Convolutional Neural Network; Network Optimization

Abstract

Fatigue driving is the main factor in many traffic accidents. The eye behavior is the main direction in the field of fatigue driving research. It reflects the degree of fatigue of the human brain to some extent. In this paper, the publicized CEW human eye opening and closing data set is used to preprocess the human eye image, and then the preprocessed image is placed in the improved LeNet-5 convolutional neural network. In order to fully extract the human eye features, the network is added. The number of layers, at the same time, to speed up the network convergence speed in order to prevent the gradient from disappearing, the activation function is changed from Tanh to ReLU function. Experiments show that the algorithm has a blink recognition rate of 93.5% in the public CEW dataset, and an accuracy rate of 5.1% compared with the unmodified LeNet-5. This method has a good blink detection effect and has important application value in the field of fatigue driving.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Lei Chao
AU  - Wang Changyuan
AU  - Lin Zhi
AU  - Huang Wenbo
PY  - 2019/11
DA  - 2019/11
TI  - Blink Fatigue Detection Algorithm Based on Improved Lenet-5
BT  - Proceedings of the 2019 International Conference on Precision Machining, Non-Traditional Machining and Intelligent Manufacturing (PNTIM 2019)
PB  - Atlantis Press
SP  - 326
EP  - 330
SN  - 2589-4943
UR  - https://doi.org/10.2991/pntim-19.2019.67
DO  - 10.2991/pntim-19.2019.67
ID  - Chao2019/11
ER  -