Authors: Jaitee Bankar, Siddhant Bhanage, Rudratej Patil, Shubham Shirke, Dnyanesh Shitole
DOI Link: https://doi.org/10.22214/ijraset.2024.57981
Certificate: View Certificate
This research introduces a Driver Drowsiness Detection system employing Convolutional Neural Networks (CNN). The system analyses real-time facial features from in-vehicle cameras to determine a driver\'s alertness level. Trained on diverse datasets, the CNN model demonstrates high accuracy in identifying drowsiness signs, making it suitable for real-world deployment. This system contributes to road safety by providing timely alerts to prevent accidents caused by driver fatigue. As road safety remains a critical concern, the development of intelligent systems to mitigate driver-related risks has become imperative. Driver drowsiness is a major factor contributing to road accidents, emphasizing the need for robust and real-time detection mechanisms. This research presents a novel approach for Driver Drowsiness Detection using Convolutional Neural Networks (CNN). The proposed system utilizes CNN architecture to analyse facial features extracted from real-time video streams captured by an in-vehicle camera. Facial landmarks and expressions are processed to determine the driver\'s level of alertness. The CNN model is trained on a diverse dataset comprising both drowsy and alert facial expressions, ensuring its adaptability to different driving conditions and individual characteristics. The system\'s effectiveness is evaluated through extensive experiments using various datasets and scenarios. The results demonstrate the CNN\'s capability to accurately identify signs of driver drowsiness, achieving high precision and recall rates. Furthermore, the model\'s real-time processing capabilities make it suitable for deployment in practical, on-road scenarios. The proposed Driver Drowsiness Detection system presents a promising solution to enhance road safety by providing timely alerts to drivers when signs of drowsiness are detected. This research contributes to the ongoing efforts to integrate artificial intelligence into vehicle safety systems, ultimately reducing the risk of accidents caused by driver fatigue.
I. INTRODUCTION
As advancements in technology continue to reshape the automotive landscape, there is a growing emphasis on integrating intelligent systems to enhance road safety. One critical aspect of this endeavour is the development of Driver Drowsiness Detection systems, aimed at mitigating the risks associated with fatigued driving. Driver drowsiness remains a significant factor contributing to road accidents globally, underscoring the urgency to implement effective and reliable detection mechanisms. This research focuses on leveraging the power of Convolutional Neural Networks (CNNs) to address the challenge of Driver Drowsiness Detection. Traditional methods often rely on simple thresholding techniques or basic image processing, which may lack the robustness required for real-world scenarios.
In contrast, CNNs, with their ability to automatically learn hierarchical features from data, offer a promising solution for accurately assessing a driver's level of alertness based on facial expressions.
The ubiquity of in-vehicle cameras provides an opportunity to monitor drivers continuously and in real-time. By utilizing CNNs to analyze facial features extracted from these video streams, this research aims to create a system capable of effectively distinguishing between alert and drowsy states. The adaptability of the proposed model to diverse driving conditions and individual characteristics is crucial for its practical implementation. In the following sections, we delve into the methodology behind the CNN-based Driver Drowsiness Detection system, discussing the dataset used for training and validation, the model architecture, and the evaluation metrics employed. The outcomes of this research not only contribute to the field of computer vision and artificial intelligence but also hold significant potential for enhancing road safety by providing timely alerts to drivers experiencing drowsiness.
II. RELATED WORK
III. FLOWCHART
Driver drowsiness detection systems typically utilize various sensors and algorithms to monitor a driver's behaviour and physiological signals, aiming to identify signs of fatigue or drowsiness. The working of these systems often involves the following components:
It's important to note that the effectiveness of driver drowsiness detection systems depends on the quality of sensors, robustness of algorithms, and the ability to adapt to different driving conditions and individual variations in behavior. Additionally, user acceptance and ethical considerations, such as privacy concerns, are crucial aspects that need to be addressed in the development and implementation of these systems.\
IV. ACKNOWLEDGMENT
We would like to express our sincere gratitude to all those who contributed to the successful completion of this research on Driver Drowsiness Detection using Convolutional Neural Networks (CNN). First and foremost, we extend our appreciation to our research team members for their dedication, collaboration, and valuable insights throughout the project. Their diverse expertise has been instrumental in shaping the methodology and findings. We are thankful for the support and resources provided by [Institution or Organization Name], which facilitated the execution of this research. Special thanks to [Supervisor/Advisor Name] for their guidance, encouragement, and constructive feedback that greatly enriched the quality of our work. Our heartfelt thanks go to the participants who willingly shared their time and experiences, contributing essential data for our study. Their involvement has been crucial in making this research more robust and applicable to real-world scenarios. Lastly, we acknowledge the broader scientific community for its continuous advancements, which have laid the foundation for the development and implementation of innovative technologies in the field of driver safety. This project represents a collaborative effort, and we are deeply appreciative of the collective contributions that have made it possible.
In conclusion, the application of Convolutional Neural Networks (CNNs) in driver drowsiness detection demonstrates significant promise for enhancing road safety. Through real-time analysis of facial features, CNNs can accurately identify signs of driver fatigue, offering a proactive mechanism to prevent potential accidents. However, ongoing research and development are essential to address challenges such as diverse datasets, real-world adaptability, and user acceptance, ensuring the continued improvement and effectiveness of CNN-based driver detection systems on the road.
[1] J. May and C. Baldwin, “Driver fatigue: The importance of identifying causal factors of fatigue when considering detection and countermeasure technologies,” Transp. Res. F, Traffic Psychol. Behav., vol. 12, no. 3, pp. 218–224, 2009. [2] S. Lal and A. Craig, “A critical review of the psychophysiology of driver fa- tigue,” Biol. Psychol., vol. 55, no. 3, pp. 173–194, 2001. [3] E. Hitchcock and G. Matthews, “Multidimensional assessment of fatigue: A review and recommendations,” in Proc. Int. Conf. Fatigue Manage.Transp. Oper., Seattle, WA, USA, Sep. 2005. [4] Williamson, A. Feyer, and R. Friswell, “The impact of work practices on fatigue in long distance truck drivers,” Accident Anal. Prevent., vol. 28, no. 6, pp. 709–719, 1996. [5] W. Dement and M. Carskadon, “Current perspectives on daytime sleepiness: The issues,” Sleep, vol. 5, no. S2, pp. S56–S66, 1982 [6] L. Hartley, T. Horberry, N. Mabbott, and G. Krueger, “Review of fatigue detec- tion and prediction technologies,” Nat. Road Transp. Commiss., Melbourne, Vic., Australia, Tech. Rep., 2000. [7] Sahayadhas, K. Sundaraj, and M. Murugappan, “Detecting driver drowsi- ness based on sensors: A review,” Sensors, vol. 12, pp. 16 937–16 953, 2012. [8] S. Kee, S. Tamrin, and Y. Goh, “Driving fatigue and performance among oc- cupational drivers in simulated prolonged driving,” Global J. HealthSci., vol. 2, no. 1, pp. 167–177, 2010. [9] M.-H. Sigari, M.-R.Pourshahabi, M. Soryani, and M. Fathy, “A review on driver face monitoring systems for fatigue and distraction detection,”Int. J. Adv. Sci. Technol., vol. 64, pp. 73–100, 2014.
Copyright © 2024 Jaitee Bankar, Siddhant Bhanage, Rudratej Patil, Shubham Shirke, Dnyanesh Shitole. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Paper Id : IJRASET57981
Publish Date : 2024-01-11
ISSN : 2321-9653
Publisher Name : IJRASET
DOI Link : Click Here