An embedded device-oriented fatigue driving detection method based on a YOLOv5s
Author:
Funder
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-023-09255-9.pdf
Reference43 articles.
1. Dasgupta A, Rahman D, Routray A (2018) A smartphone-based drowsiness detection and warning system for automotive drivers. IEEE Trans Intell Transp Syst 20(11):4045–4054
2. Wang Z, Hong Q, Wang X (2020) A memristive circuit implementation of eyes state detection in fatigue driving based on biological long short-term memory rule. IEEE/ACM Trans Comput Biol Bioinf 18(6):2218–2229
3. Shang Y, Yang M, Cui J, Cui L, Huang Z, Li X (2023) Driver emotion and fatigue state detection based on time series fusion. Electronics 12(1):26
4. Bose R, Wang H, Dragomir A, Thakor NV, Bezerianos A, Li J (2019) Regression-based continuous driving fatigue estimation: toward practical implementation. IEEE Trans Cogn Dev Syst 12(2):323–331
5. Wijnands JS, Thompson J, Nice KA, Aschwanden GD, Stevenson M (2020) Real-time monitoring of driver drowsiness on mobile platforms using 3d neural networks. Neural Comput Appl 32:9731–9743
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