Research on Human-computer Interaction and Emotion Recognition based on Convolutional Neural Networks

Authors

  • Yuhai Yang

DOI:

https://doi.org/10.62051/es4a0b41

Keywords:

Human-computer Interaction, Fatigue Check, Convolutional Neural Network.

Abstract

Amidst the swift advancement of intelligent vehicles, computer vision technology has become an increasingly important core technology in the field of autonomous driving, especially in providing safety guarantees for drivers. Given the common Incidence of traffic accidents resulting from driver fatigue. The use of deep learning technology to prevent driver fatigue behavior has become an indispensable protective application in intelligent cockpit human-computer interaction. This article introduces an algorithm designed to detect drowsy driving, which relies on Convolutional neuronal networks are employed to address the challenge of fatigue during driving. The core of the research lies in constructing a model for detecting fatigue depending on YOLOv5. This model uses UTA-RLDD and YawDD datasets and extracts refined image features from video frames as available input data for the model. It can have a better detection effect on the blinking and yawning characteristics of the driver's eyelids and lips, thereby effectively determining whether they are in a fatigue state. Compared with deep learning fatigue detection algorithms, experiments have shown that its average accuracy for eye closure and yawning reaches 97.3% and 91.7%, respectively, which can effectively detect driver fatigue, prevent drivers from entering a fatigue driving state, and improve driving safety. Future research will continue to optimize and expand the model to adapt to more complex driving scenarios and make greater contributions to the development of intelligent vehicles.

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References

D.H Li, Q. Liu, W Yuan et al. Relationship between fatigue driving and traffic accident[J]. Journal of Traffic and Transportation Engineering, 10(2), (2010) 104-109.

X.W Zhang, F.S Wang, H.Y Yang. Fatigue driving detection algorithm based on multi feature information fusion [J/OL]. Journal of Chongqing University of Technology (Natural Science Edition): (2024) 1-11.

R Zhang, T.J Zhu, Z L Zou, et al. A review of research on driver fatigue driving detection methods [J]. Computer Engineering and Applications, 58(21), (2022): 53-66.

Chen L, Xin G, Liu Y, et al. Driver fatigue detection based on facial key points and LSTM[J]. Security and Communication Networks, (2021): 1-9. DOI: https://doi.org/10.1155/2021/5383573

W Liu, D Anguelov, D. Erhan, RHAN, et al. Ssd: Single shot multibox detector[C]//Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, (2016) 21-37. DOI: https://doi.org/10.1007/978-3-319-46448-0_2

M Tan, R Pang, Q.V Le. Efficientdet: Scalable and efficient object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. (2020): 10781-10790. DOI: https://doi.org/10.1109/CVPR42600.2020.01079

T.Y Lin, P, Goyal, R. Girshick et al. Focal loss for dense object detection[C]//Proceedings of the IEEE International Conference on Computer Vision. (2017): 2980-2988. DOI: https://doi.org/10.1109/ICCV.2017.324

J. Redmon, S. Divvala, R. Girshick, et al. You only look once: unified, real-time object detection [ C] ∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). LLas Vegas:IEEE, (2016): 779-788. DOI: https://doi.org/10.1109/CVPR.2016.91

Z.H Zheng, J Zhao, Y Li. Research on detecting bearing-cover defects based on improved YOLOv3[J]. IEEE Access, 9 (2021): 10304-10315. DOI: https://doi.org/10.1109/ACCESS.2021.3050484

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Published

12-08-2024

How to Cite

Yang, Y. (2024) “Research on Human-computer Interaction and Emotion Recognition based on Convolutional Neural Networks”, Transactions on Computer Science and Intelligent Systems Research, 5, pp. 422–427. doi:10.62051/es4a0b41.