Design of Fatigue Driving Detection System Based on Cascaded Neural Network

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

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%.

Key words: convolutional neural networks(CNN), face detection, fatigue determination, cascaded network, bounding box

CLC Number:

TP277

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.

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测试任务	本文时间开销	SSD样本时间开销
左眼检测分类	2.685	6.453
右眼检测分类	2.668	6.386
嘴部检测分类	2.488	6.568

算法	检出准确率/%
文献[6]算法	95.0
文献[7]算法	91.6
文献[8]算法	92.5
本文算法	95.7

任务类别	时间开销/ms
原始图像获取及优化	3.034
MTCNN人脸检测	5.686
EMs-Net眼、嘴提取	2.563
眼部状态返回框	4.569
嘴部状态返回框	3.326
总时间开销	19.177

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