Design of Fatigue Driving Detection System Based on Cascaded Neural Network

Authors

Bangqian Ao, 1.College of Engineering, Zunyi Normal University, Zunyi 563006, China;Follow
Sha Yang, 2.College of Physics and Electronics, Zunyi Normal University, Zunyi 563006, China;
Jinqing Linghu, 1.College of Engineering, Zunyi Normal University, Zunyi 563006, China;
zhenhuan Ye, 1.College of Engineering, Zunyi Normal University, Zunyi 563006, China;

Abstract

Abstract: An algorithm is proposed to greatly improves the face detection rate and ensures the accuracy by adjusting the size of input images, expanding the minimum face size, and reducing the scaling ratio between layers of the detection window. The detection efficiency of this algorithm is 18 times higher than that of the original MTCNN. By building a new CNN structure model for the detection of eyes and mouths, we can achieve network detection accuracy of 95.6%. The proposed network is cascaded with the original MTCNN to continue classifying and locating the eyes and mouth in the formerly detected face area. The false detection due to a single feature are improved through the setting of a comprehensive fatigue detection function, and the detection accuracy can reach 95.7%.

Recommended Citation

Ao, Bangqian; Yang, Sha; Linghu, Jinqing; and Ye, zhenhuan (2022) "Design of Fatigue Driving Detection System Based on Cascaded Neural Network," Journal of System Simulation: Vol. 34: Iss. 2, Article 15.
DOI: 10.16182/j.issn1004731x.joss.20-0703
Available at: https://dc-china-simulation.researchcommons.org/journal/vol34/iss2/15

First Page

323

Revised Date

2020-12-03

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.20-0703

Last Page

333

CLC

TP277

Recommended Citation

Bangqian Ao, Sha Yang, Jinqing Linghu, zhenhuan Ye. Design of Fatigue Driving Detection System Based on Cascaded Neural Network[J]. Journal of System Simulation, 2022, 34(2): 323-333.

DOI

10.16182/j.issn1004731x.joss.20-0703