A Method of Fatigue Driving State Detection Based on Deep Learning
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...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Control and Information Technology
2018-01-01
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| Series: | Kongzhi Yu Xinxi Jishu |
| Subjects: | |
| Online Access: | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2018.06.400 |
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| _version_ | 1849224827948236800 |
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| author | XIONG Qunfang LIN Jun YUE Wei |
| author_facet | XIONG Qunfang LIN Jun YUE Wei |
| author_sort | XIONG Qunfang |
| collection | DOAJ |
| description | 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%. |
| format | Article |
| id | doaj-art-c975f2bc132d490c8c6c6e247c78f5ea |
| institution | Kabale University |
| issn | 2096-5427 |
| language | zho |
| publishDate | 2018-01-01 |
| publisher | Editorial Office of Control and Information Technology |
| record_format | Article |
| series | Kongzhi Yu Xinxi Jishu |
| spelling | doaj-art-c975f2bc132d490c8c6c6e247c78f5ea2025-08-25T06:54:15ZzhoEditorial Office of Control and Information TechnologyKongzhi Yu Xinxi Jishu2096-54272018-01-0135919582327155A Method of Fatigue Driving State Detection Based on Deep LearningXIONG QunfangLIN JunYUE WeiCurrent 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%.http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2018.06.400fatigue detectdeep learningconvolutional neural networkPerclos algorithm |
| spellingShingle | XIONG Qunfang LIN Jun YUE Wei A Method of Fatigue Driving State Detection Based on Deep Learning Kongzhi Yu Xinxi Jishu fatigue detect deep learning convolutional neural network Perclos algorithm |
| title | A Method of Fatigue Driving State Detection Based on Deep Learning |
| title_full | A Method of Fatigue Driving State Detection Based on Deep Learning |
| title_fullStr | A Method of Fatigue Driving State Detection Based on Deep Learning |
| title_full_unstemmed | A Method of Fatigue Driving State Detection Based on Deep Learning |
| title_short | A Method of Fatigue Driving State Detection Based on Deep Learning |
| title_sort | method of fatigue driving state detection based on deep learning |
| topic | fatigue detect deep learning convolutional neural network Perclos algorithm |
| url | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2018.06.400 |
| work_keys_str_mv | AT xiongqunfang amethodoffatiguedrivingstatedetectionbasedondeeplearning AT linjun amethodoffatiguedrivingstatedetectionbasedondeeplearning AT yuewei amethodoffatiguedrivingstatedetectionbasedondeeplearning AT xiongqunfang methodoffatiguedrivingstatedetectionbasedondeeplearning AT linjun methodoffatiguedrivingstatedetectionbasedondeeplearning AT yuewei methodoffatiguedrivingstatedetectionbasedondeeplearning |