Analysis of Driver Drowsiness Using Convolution Neural Network Algorithms

Gazee Afzal Wani; Ravinder PaL Singh; Dr. Monika Mehra

doi:10.55524/ijirem.2022.9.2.11

Abstract

Our security is the need while voyaging or driving. One slip-up of the driver can prompt serious actual wounds, passing and critical monetary misfortunes. These days there are numerous frameworks accessible in market like route frameworks, different sensors and so forth to make driver's work simple. There are different reasons particularly human blunders which gives ascends to the street mishaps. Reports say that there is an enormous addition in the street mishaps in our country since most recent couple of years. The principal reason happening from the interstate mishaps is the laziness and tiredness of driver while driving. It is a vital stage to accompany a proficient procedure to recognize sleepiness when driver feels tired. This could save enormous number of mishaps to happen. In this framework, we proposed to decrease the quantity of mishaps brought about by driver weariness and hence further develop street wellbeing. We find, track, and break down both the driver face and eyes to quantify PERCLOS (level of eye conclusion).

Keywords

PERCLOS, AI, Driver weakness, driver drowsiness, CNN Algorithm

References

Marco Javier Flores • José María Armingol • Arturo de la Escalera.: Real-Time Warning System for Driver Drowsiness Using Visual Information. In: Springer Science + Business Media B.V. 2009
Luis M. Bergasa, Jesús Nuevo, Miguel A. Sotelo, RafaelBarea, and María Elena Lopez.:Real-Time System forMonitoring Driver Vigilance. In: ieee transactions on intelligent transportation systems, vol. 7, no. 1, march 2006
Mohamad-Hoseyn Sigari, Mahmood Fathy, and Mohsen Soryani.: A Driver Face Monitoring System for Fatigue and Distraction Detection. In: Hindawi Publishing Corporation International Journal of Vehicular Technology, Volume 2013, Article ID 263983, 11 pages
Jay D. Fuletra, Bulari Bosamia: A Survey On Driver’s Drowsiness Detection Techniques presented at IJRITCC in November 2013.
Ming-ai Li, Cheng Zhang, Jin-Fu Yang. :An EEG-based Method for Detecting Drowsy Driving State. In: Fuzzy Systems and Knowledge Discovery (FSKD), 2010 Seventh International Conference on , 5, pp. 2164- 2167, 10-12 Aug. 2010.
Er. Manoram Vats and Er. Anil Garg.: Detection And Security System For Drowsy Driver By Using Artificial Neural Network Technique. In: International Journal of Applied Science and Advance Technology January-June 2012, Vol. 1, No. 1, pp. 39-43
Examining individual differences. J. Sleep Res. 2006, 15, 47–53.

Cites this article as

G. A. Wani, R. P. Singh, D. M. Mehra, "Analysis of Driver Drowsiness Using Convolution Neural Network Algorithms", International Journal of Innovative Research in Engineering & Management (IJIREM), Vol-9, Issue-2, Page No-84-90, 2022. Available from: https://doi.org/10.55524/ijirem.2022.9.2.11