Fatigue Driving Detection Based on Deep Learning and Multi-Index Fusion

In order to reduce traffic accidents caused by fatigue driving, a fatigue driving detection algorithm is proposed based on deep learning and facial multi-index fusion from the driver<inline-formula> <tex-math notation="LaTeX">$'\text{s}$ </tex-math></inline-formul...

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Autores principales:	Huijie Jia, Zhongjun Xiao, Peng Ji
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Fatigue driving detection improved MTCNN E-MSR Net facial multi-index fusion Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/971d6f2b58294995bf6a75ddf90d27e1
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id	oai:doaj.org-article:971d6f2b58294995bf6a75ddf90d27e1
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spelling	oai:doaj.org-article:971d6f2b58294995bf6a75ddf90d27e12021-11-09T00:02:55ZFatigue Driving Detection Based on Deep Learning and Multi-Index Fusion2169-353610.1109/ACCESS.2021.3123388https://doaj.org/article/971d6f2b58294995bf6a75ddf90d27e12021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9590571/https://doaj.org/toc/2169-3536In order to reduce traffic accidents caused by fatigue driving, a fatigue driving detection algorithm is proposed based on deep learning and facial multi-index fusion from the driver<inline-formula> <tex-math notation="LaTeX">$'\text{s}$ </tex-math></inline-formula> facial features. Because the scene in the actual driving process is very complex and changeable, this algorithm first improves the multi-task cascaded convolutional neural network (MTCNN) so that it can quickly and accurately locate the face and detect the facial key points. According to the facial key points, the driver’s eyes and mouth regions are determined. Second, these regions are input into the eyes and mouth state recognition network (E-MSR Net) for state recognition. The E-MSR Net is a depth separable convolution neural network that is improved and optimized based on MobilenetV2. Finally, the three facial features of eye closure rate (ECR), mouth opening rate (MOR), and head non-positive face rate (HNFR) are fused to judge the driver’s fatigue state. This algorithm can quickly and accurately make judgments in the face of complex and changeable scenes. At the same time, it can avoid the failure of the algorithm caused by the occlusion of the eyes or mouth due to wearing sunglasses or masks during driving. The accuracy of the proposed algorithm on the self-made data set achieved 97.5%, which proved the feasibility of the algorithm.Huijie JiaZhongjun XiaoPeng JiIEEEarticleFatigue driving detectionimproved MTCNNE-MSR Netfacial multi-index fusionElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 147054-147062 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Fatigue driving detection improved MTCNN E-MSR Net facial multi-index fusion Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Fatigue driving detection improved MTCNN E-MSR Net facial multi-index fusion Electrical engineering. Electronics. Nuclear engineering TK1-9971 Huijie Jia Zhongjun Xiao Peng Ji Fatigue Driving Detection Based on Deep Learning and Multi-Index Fusion
description	In order to reduce traffic accidents caused by fatigue driving, a fatigue driving detection algorithm is proposed based on deep learning and facial multi-index fusion from the driver<inline-formula> <tex-math notation="LaTeX">$'\text{s}$ </tex-math></inline-formula> facial features. Because the scene in the actual driving process is very complex and changeable, this algorithm first improves the multi-task cascaded convolutional neural network (MTCNN) so that it can quickly and accurately locate the face and detect the facial key points. According to the facial key points, the driver’s eyes and mouth regions are determined. Second, these regions are input into the eyes and mouth state recognition network (E-MSR Net) for state recognition. The E-MSR Net is a depth separable convolution neural network that is improved and optimized based on MobilenetV2. Finally, the three facial features of eye closure rate (ECR), mouth opening rate (MOR), and head non-positive face rate (HNFR) are fused to judge the driver’s fatigue state. This algorithm can quickly and accurately make judgments in the face of complex and changeable scenes. At the same time, it can avoid the failure of the algorithm caused by the occlusion of the eyes or mouth due to wearing sunglasses or masks during driving. The accuracy of the proposed algorithm on the self-made data set achieved 97.5%, which proved the feasibility of the algorithm.
format	article
author	Huijie Jia Zhongjun Xiao Peng Ji
author_facet	Huijie Jia Zhongjun Xiao Peng Ji
author_sort	Huijie Jia
title	Fatigue Driving Detection Based on Deep Learning and Multi-Index Fusion
title_short	Fatigue Driving Detection Based on Deep Learning and Multi-Index Fusion
title_full	Fatigue Driving Detection Based on Deep Learning and Multi-Index Fusion
title_fullStr	Fatigue Driving Detection Based on Deep Learning and Multi-Index Fusion
title_full_unstemmed	Fatigue Driving Detection Based on Deep Learning and Multi-Index Fusion
title_sort	fatigue driving detection based on deep learning and multi-index fusion
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/971d6f2b58294995bf6a75ddf90d27e1
work_keys_str_mv	AT huijiejia fatiguedrivingdetectionbasedondeeplearningandmultiindexfusion AT zhongjunxiao fatiguedrivingdetectionbasedondeeplearningandmultiindexfusion AT pengji fatiguedrivingdetectionbasedondeeplearningandmultiindexfusion
_version_	1718441404008497152

Fatigue Driving Detection Based on Deep Learning and Multi-Index Fusion

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