Realtime Driver Drowsiness Detection using YOLOv5 and Computer Vision  Techniques

Nidhi Gupta, Chander Shekhar, Sudesh Pahal, Sachin Kumar Baghel, Priyanka Nandal

Realtime Driver Drowsiness Detection using YOLOv5 and Computer Vision Techniques

Authors

Nidhi Gupta, Chander Shekhar, Sudesh Pahal, Sachin Kumar Baghel, Priyanka Nandal

Keywords:

driver drowsiness; face detection; yawning detection; yolov5; fatigue.

Abstract

This report is an overview of research and projects conducted in computing to develop drowsiness detection systems toproactively prevent crashes caused by driver fatigue and drowsiness. Common methods of fatigue detection use complexmethods such as EEG and ECG. This method has high measurement accuracy, but requires the use of contact measurementsand has many limitations for monitoring driver fatigue and sleepiness

References

. Altameem, A. Kumar, R. C. Poonia, S. Kumar and A. K. J. Saudagar, "Early Identification and Detection of Driver Drowsiness by Hybrid Machine Learning," in IEEE Access, vol. 9, pp. 162805-162819, 2021, doi: 10.1109/ACCESS.2021.3131601. M. Ramzan, H. U. Khan, S. M. Awan, A. Ismail, M. Ilyas and A. Mahmood, "A Survey on State-of-the-Art

Drowsiness Detection Techniques," in IEEE Access, vol. pp. 61904-61919, 10.1109/ACCESS.2019.2914373.

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Published

2024-08-08

How to Cite

Nidhi Gupta, Chander Shekhar, Sudesh Pahal, Sachin Kumar Baghel, Priyanka Nandal. (2024). Realtime Driver Drowsiness Detection using YOLOv5 and Computer Vision Techniques. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 3165–3183. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2436