Chinese Journal of Liquid Crystals and Displays, Volume. 40, Issue 4, 617(2025)
Fatigue driving detection based on improved YOLOv8n-Pose
[1] YU F W, ZHENG W T, LI T. Review of research on automotive anti-fatigue driving systems[J]. For Repair & Maintenance, 69-71(2024).
[12] LYU X L, LIU X F, BAI Y Q. Research on driving fatigue detection based on SSD muti-factor fusion[J]. Electronic Measurement Technology, 45, 138-143(2022).
[16] WANG Y J. Research on fatigue driving detection based on spatiotemporal graph convolutional neural network[D](2023).
[17] ZHANG H M, YAN D D, TIAN Q Q. Improved spatio-temporal graph convolutional networks for video anomaly detection[J]. Opto-Electronic Engineering, 51, 240034(2024).
[19] LI H L, LI J, WEI H B et al. Slim-neck by GSConv: a lightweight-design for real-time detector architectures[J/OL]. arXiv, 2206-02424(2022).
[22] TIAN Z, SHEN C H, CHEN H et al. FCOS: a simple and strong anchor-free object detector[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44, 1922-1933(2022).
[25] ZHONG Y K, MO H N. A video anomaly detection method based on deep autoencoding Gaussian mixture model[J]. Infrared and Laser Engineering, 51, 20210547(2022).
Get Citation
Copy Citation Text
Zhongqi CAI, Shanling LIN, Jianpu LIN, Shanhong LÜ, Zhixian LIN, Tailiang GUO. Fatigue driving detection based on improved YOLOv8n-Pose[J]. Chinese Journal of Liquid Crystals and Displays, 2025, 40(4): 617
Category:
Received: Jul. 12, 2024
Accepted: --
Published Online: May. 21, 2025
The Author Email: Zhixian LIN (lzx2005000@163.com)