Fatigue detection method for UAV remote pilot based on multi feature fusion
In recent years, UAV industry is developing rapidly and vigorously. However, so far, there is no relevant research on the fatigue detection method for UAV remote pilot, which is the core technology to ensure the flight safety of UAV. Aiming at this problem, a fatigue detection method for UAV remote...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-01-01
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| Series: | Electronic Research Archive |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/era.2023022?viewType=HTML |
| _version_ | 1811169606720552960 |
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| author | Lei Pan Chongyao Yan Yuan Zheng Qiang Fu Yangjie Zhang Zhiwei Lu Zhiqing Zhao Jun Tian |
| author_facet | Lei Pan Chongyao Yan Yuan Zheng Qiang Fu Yangjie Zhang Zhiwei Lu Zhiqing Zhao Jun Tian |
| author_sort | Lei Pan |
| collection | DOAJ |
| description | In recent years, UAV industry is developing rapidly and vigorously. However, so far, there is no relevant research on the fatigue detection method for UAV remote pilot, which is the core technology to ensure the flight safety of UAV. Aiming at this problem, a fatigue detection method for UAV remote pilot is proposed in this paper. Specifically, we first build a UAV operator fatigue detection database (OFDD). By analyzing the fatigue features in the database, we find that multiple facial features are highly correlated to the fatigue state, especially the head posture, and the temporal information is essential for distinguish between yawn and speaking in the study of UAV remote pilot fatigue detection. Based on these findings, a fatigue detection method for UAV remote pilots was proposed by efficiently locating the related facial regions, a multiple features extraction module to extract the eye, mouth and head posture features, and an efficient temporal fatigue decision module based on SVM. The experimental results show that this method not only performs well on the traditional driver dataset, but also achieves an accuracy rate of 97.05%; and it achieves the highest detection accuracy rate of 97.32% on the UAV remote pilots fatigue detection dataset OFDD. |
| first_indexed | 2024-04-10T16:45:13Z |
| format | Article |
| id | doaj.art-3bdb4b36dd834820badbed8b97d155d5 |
| institution | Directory Open Access Journal |
| issn | 2688-1594 |
| language | English |
| last_indexed | 2024-04-10T16:45:13Z |
| publishDate | 2023-01-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Electronic Research Archive |
| spelling | doaj.art-3bdb4b36dd834820badbed8b97d155d52023-02-08T01:07:28ZengAIMS PressElectronic Research Archive2688-15942023-01-0131144246610.3934/era.2023022Fatigue detection method for UAV remote pilot based on multi feature fusionLei Pan0Chongyao Yan1Yuan Zheng2Qiang Fu3Yangjie Zhang 4Zhiwei Lu5Zhiqing Zhao 6Jun Tian7School of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, ChinaSchool of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, ChinaSchool of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, ChinaSchool of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, ChinaSchool of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, ChinaSchool of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, ChinaSchool of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, ChinaSchool of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, ChinaIn recent years, UAV industry is developing rapidly and vigorously. However, so far, there is no relevant research on the fatigue detection method for UAV remote pilot, which is the core technology to ensure the flight safety of UAV. Aiming at this problem, a fatigue detection method for UAV remote pilot is proposed in this paper. Specifically, we first build a UAV operator fatigue detection database (OFDD). By analyzing the fatigue features in the database, we find that multiple facial features are highly correlated to the fatigue state, especially the head posture, and the temporal information is essential for distinguish between yawn and speaking in the study of UAV remote pilot fatigue detection. Based on these findings, a fatigue detection method for UAV remote pilots was proposed by efficiently locating the related facial regions, a multiple features extraction module to extract the eye, mouth and head posture features, and an efficient temporal fatigue decision module based on SVM. The experimental results show that this method not only performs well on the traditional driver dataset, but also achieves an accuracy rate of 97.05%; and it achieves the highest detection accuracy rate of 97.32% on the UAV remote pilots fatigue detection dataset OFDD.https://www.aimspress.com/article/doi/10.3934/era.2023022?viewType=HTMLfatigue detectionuav remote pilotdatabasefeature fusion |
| spellingShingle | Lei Pan Chongyao Yan Yuan Zheng Qiang Fu Yangjie Zhang Zhiwei Lu Zhiqing Zhao Jun Tian Fatigue detection method for UAV remote pilot based on multi feature fusion Electronic Research Archive fatigue detection uav remote pilot database feature fusion |
| title | Fatigue detection method for UAV remote pilot based on multi feature fusion |
| title_full | Fatigue detection method for UAV remote pilot based on multi feature fusion |
| title_fullStr | Fatigue detection method for UAV remote pilot based on multi feature fusion |
| title_full_unstemmed | Fatigue detection method for UAV remote pilot based on multi feature fusion |
| title_short | Fatigue detection method for UAV remote pilot based on multi feature fusion |
| title_sort | fatigue detection method for uav remote pilot based on multi feature fusion |
| topic | fatigue detection uav remote pilot database feature fusion |
| url | https://www.aimspress.com/article/doi/10.3934/era.2023022?viewType=HTML |
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