Drowsiness Detection System for Vehicles Using Raspberry PI

Abstract

Drowsy driving is a leading cause of road accidents worldwide, leading to severe consequences, including fatalities and property damage. This research introduces a real-time drowsiness detection system aimed at alerting drivers and reducing accident risks. The system leverages computer vision and machine learning algorithms to identify early indicators of drowsiness, such as eye closure, head movements, and facial expressions. A Convol utional Neural Network (CNN) is trained using a dataset containing images of drivers, achieving a 95% accuracy rate in drowsiness detection. To enhance driver awareness, the system is equipped with real-time alerts, including visual, auditory, and vibrational warnings when drowsiness is detected. Extensive testing on driver datasets demonstrates the system\'s effectiveness in recognizing drowsiness and minimizing accident risks. This study highlights the potential of machine learning and computer vision in real-time drowsiness monitoring, contributing to improved road safety.

Introduction

The Drowsiness Detector System represents a significant advancement in driver safety technology. Through rigorous development and testing, the system has proven effective in identifying fatigue-related behaviors with high accuracy. By leveraging real-time computer vision techniques, it provides non-intrusive monitoring that does not require specialized hardware, making it accessible for widespread implementation. Key advantages of the system include its ability to process video at high frame rates, maintain low latency, and deliver timely alerts to drowsy drivers. Its modular architecture allows for scalability and future enhancements. The system's multi-faceted approach to drowsiness detection analyzing EAR, MAR, and head movements ensures greater accuracy compared to single-factor detection methods. Despite its strengths, the system has some limitations. Performance may degrade in low-light environments, though this issue can be mitigated by incorporating infrared cameras. Additionally, the accuracy of detection may be affected by occlusions, such as sunglasses or face coverings.

In conclusion, the Drowsiness Detector System is a significant step toward reducing fatigue-related traffic accidents worldwide. With continued research and development, this technology has the potential to enhance road safety on a global scale.

Drowsy driving is a major cause of road accidents, as fatigue impairs reaction time and focus. To mitigate this, a real-time, non-invasive Drowsiness Detection System has been developed using computer vision and machine learning to monitor driver fatigue signs like prolonged eye closure, yawning, and head nodding. The system utilizes a webcam to track facial landmarks and employs algorithms such as Naive Bayes and deep learning models (CNN and LSTM) for accurate detection. Alerts are issued via audio and visual signals to prompt driver awareness.

The system addresses limitations of previous methods, which often require intrusive hardware or lack real-time accuracy. It integrates features such as lighting condition adaptation, accessibility, and scalability for personal and commercial use. Hardware components include a Raspberry Pi, webcam, and buzzer for real-time processing and alerting.

Extensive testing shows the system achieves over 90% accuracy in detecting fatigue indicators and operates efficiently with low latency. Future improvements may include additional sensors (e.g., heart rate monitors), enhanced AI models, and wireless connectivity for personalized long-term monitoring.

The solution represents a significant advancement in road safety by providing a cost-effective, scalable, and user-friendly approach to prevent accidents caused by drowsy driving.

Conclusion

The Drowsiness Detector System represents a significant advancement in driver safety technology. Through rigorous development and testing, the system has proven effective in identifying fatigue-related behaviors with high accuracy. By leveraging real-time computer vision techniques, it provides non-intrusive monitoring that does not require specialized hardware, making it accessible for widespread implementation. Key advantages of the system include its ability to process video at high frame rates, maintain low latency, and deliver timely alerts to drowsy drivers. Its modular architecture allows for scalability and future enhancements. The system\'s multi-faceted approach to drowsiness detection analyzing EAR, MAR, and head movements ensures greater accuracy compared to single-factor detection methods. Despite its strengths, the system has some limitations. Performance may degrade in low-light environments, though this issue can be mitigated by incorporating infrared cameras. Additionally, the accuracy of detection may be affected by occlusions, such as sunglasses or face coverings. In conclusion, the Drowsiness Detector System is a significant step toward reducing fatigue-related traffic accidents worldwide. With continued research and development, this technology has the potential to enhance road safety on a global scale.

References

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Copyright

Copyright © 2025 Athul Krishna EM, Gokul S Kumar, Sanjay Kumar P, Harikrishnan K P, Dr. L. C. Manikandan. 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.