[1]熊群芳,林 军,岳 伟.基于深度学习的疲劳驾驶状态检测方法[J].控制与信息技术,2018,(06):91-95.[doi:10.13889/j.issn.2096-5427.2018.06.400]
 XIONG Qunfang,LIN Jun,YUE Wei.A Method of Fatigue Driving State Detection Based on Deep Learning[J].High Power Converter Technology,2018,(06):91-95.[doi:10.13889/j.issn.2096-5427.2018.06.400]
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基于深度学习的疲劳驾驶状态检测方法()
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《控制与信息技术》[ISSN:2095-3631/CN:43-1486/U]

卷:
期数:
2018年06期
页码:
91-95
栏目:
人工智能技术与应用
出版日期:
2018-12-05

文章信息/Info

Title:
A Method of Fatigue Driving State Detection Based on Deep Learning
文章编号:
2096-5427(2018)06-0091-05
作者:
熊群芳林 军岳 伟
(中车株洲电力机车研究所有限公司,湖南 株洲 412001)
Author(s):
XIONG Qunfang LIN Jun YUE Wei
( CRRC Zhuzhou Institute Co., Ltd., Zhuzhou, Hunan 412001, China )
关键词:
疲劳检测深度学习卷积神经网络Perclos算法
Keywords:
fatigue detect deep learning convolutional neural network Perclos algorithm
分类号:
TP181
DOI:
10.13889/j.issn.2096-5427.2018.06.400
文献标志码:
A
摘要:
目前疲劳驾驶检测算法大多基于单一的人工提取疲劳状态特征实现,且大部分算法结构复杂、鲁棒性低。为此,文章提出一种基于深度学习的疲劳检测方法,它采用卷积神经网络和Landmark算法来实现人脸图像特征点的自动提取,并使用SVM算法对疲劳特征进行分类,最后基于Perclos算法实现视频流图像的疲劳状态检测。实验结果表明,该方法能较好地提取疲劳特征,实现实时疲劳检测,且检测精度达96.8%。
Abstract:
Current domestic and overseas fatigue recognition algorithms are implemented using fatigue features which are mostly singular and man-made. Most of those algorithms have complex structure, low efficiency and weak adaptability in face of driver’s individual behavior habit. To this end, this paper put forward a fatigue recognition algorithm based on deep learning. Firstly, the face image feature points are automatic extracted using convolutional neural network and landmark algorithm. Then the SVM algorithm is used to classify the fatigue characteristics. Finally, the fatigue state of the video stream image is detected based on the Perclos algorithm. The experimental results show that this method can obtain good fatigue characteristics, realize real-time fatigue detection, and its detection accuracy is 96.8%.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2018-07-11
作者简介:熊群芳(1990—),女,硕士,研究方向为图像处理。
基金项目:国家重点研发计划(2018YFB1201600)
更新日期/Last Update: 2018-12-25