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YOLOv8 in Focus: A Review of its Application in Driver Monitoring Systems

Sameer Awasthi, Abhishek Verma, Ritesh Kr. Singh, Vidhik .

Abstract


This research presents a novel Driver Monitoring System (DMS) that utilises Convolutional Neural Networks (CNNs) to achieve remarkable results. Specifically, the YOLOv8 (You Only Look Once version 8) detection technique is used. The main goal is to increase road safety by using cutting-edge computer vision techniques to analyse driver behaviour in real-time. The YOLOv8 detection method, a cutting-edge CNN model renowned for its precision and effectiveness in object recognition, is the basis of the DMS. It focuses on using YOLOv8 to do tasks like gaze tracking, gesture detection, and facial recognition, making it possible to seamlessly monitor and interpret a range of driver behaviours. With the use of YOLOv8, the DMS can recognise and monitor important cues including gestures, eye movements, and facial expressions to evaluate the level of driver attention, weariness, and potential distractions. By doing a thorough analysis and assessing the YOLOv8-based DMS's efficacy in various driving situations. The system's capacity to reliably identify and mitigate safety concerns related to driver inattention impairment is exhibited through case studies and performance indicators. Furthermore, it discusses the ethical and regulatory ramifications of integrating the YOLOv8 detection algorithm into the DMS. Recommendations for industry standards compliance and privacy protection are addressed, with a focus on the proper application of this technology in the automobile sector. Overall, by demonstrating the effectiveness of the YOLOv8 detection algorithm in real-time behaviour analysis, this study advances driver monitoring systems. By utilising state-of-the-art CNN technology, the insights offered in this article open the door to safer and more intelligent modes of transportation.


Keywords


Driver Monitoring Systems, CNN, Neural network, YOLOv8, Detection Algorithm

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References


Redmon J, Farhadi A. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767. 2018 Apr 8.

Bojarski, M., Testa, D.W., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L.D., Monfort, M., Muller, U., Zhang, J., Zhang, X., Zhao, J., & Zieba, K. (2016). End to End Learning for Self-Driving Cars. ArXiv, abs/1604.07316.

Lin, T. Y., et al. (2017). Focal Loss for Dense Object Detection. https://openaccess.thecvf.com/content_ICCV_2017/papers/Lin_Focal_Los_for_ICCV_2017_paper.pdf

Redmon, J., & Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 779-788.

Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton : ImageNet Classification with Deep Convolutional Neural Networks URL: https://github.com/ultralytics/yolov5

M. B. Blaschko and C. H. Lampert. Learning to localize objects with structured output regression. In Computer Vision– ECCV 2008, pages 2–15. Springer, 2008.

L. Bourdev and J. Malik. Poselets: Body part detectors trained using 3d human pose annotations. In International Conference on Computer Vision (ICCV), 2009.

H. Cai, Q. Wu, T. Corradi, and P. Hall. The crossdepiction problem: Computer vision algorithms for recognising objects in artwork and in photographs. arXiv preprint arXiv:1505.00110, 20

N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 1, pages 886–893. IEEE, 2005.

T. Dean, M. Ruzon, M. Segal, J. Shlens, S. Vijayanarasimhan, J. Yagnik, et al. Fast, accurate detection of 100,000 object classes on a single machine. In Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on, pages 1814–1821. IEEE, 2013.

J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell. Decaf: A deep convolutional activation feature for generic visual recognition. arXiv preprint arXiv:1310.1531, 2013.

J. Dong, Q. Chen, S. Yan, and A. Yuille. Towards unified object detection and semantic segmentation. In Computer Vision–ECCV 2014, pages 299–314. Springer, 2014.

D. Erhan, C. Szegedy, A. Toshev, and D. Anguelov. Scalable object detection using deep neural networks. In Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, pages 2155–2162. IEEE, 2014.

M. Everingham, S. M. A. Eslami, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The pascal visual object classes challenge: A retrospective. International Journal of Computer Vision, 111(1):98–136, Jan. 2015.

P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained part based models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(9):1627–1645, 2010.

S. Gidaris and N. Komodakis. Object detection via a multiregion & semantic segmentation-aware CNN model. CoRR, abs/1505.01749, 2015.

S. Ginosar, D. Haas, T. Brown, and J. Malik. Detecting people in cubist art. In Computer Vision-ECCV 2014 Workshops, pages 101–116. Springer, 2014.

R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, pages 580–587. IEEE, 2014.




DOI: https://doi.org/10.37591/joedt.v14i3.7843

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