Driver Drowsiness Detection System in Real-Time Via CNN and Transfer Learning

Abstract

Nowadays, accidents occur during drowsy road trips and increase day by day; It is a known fact that many accidents occur due to driver fatigue and sometimes inattention, this research is primarily devoted to maximizing efforts to identify drowsiness. State of the driver under real driving conditions .The aim of driver drowsiness detection systems is to try to reduce these traffic accidents. The secondary data collected focuses on previous research on systems for detecting drowsiness and several methods have been used to detect drowsiness or inattentive driving. Our goal is to provide an interface where the program can automatically detect the driver\'s drowsiness and detect it in the event of an accident by using the image of a person captured by the webcam and examining how this information can be used to improve driving safety can be used. a vehicle safety project that helps prevent accidents caused by the driver\'s sleep. Basically, you\'re collecting a human image from the web cam and exploring how that in formation could be used to improve driving safety. Collect images from the live webcam stream and apply machine learning algorithm to the image and recognize the drowsy driver or not. When the driver is sleepy, it plays the buzzer alarm and increases the buzzer sound. If the driver doesn\'t wake up, they\'ll send a text message and email to their family members about their situation. Hence, this utility goes beyond the problem of detecting drowsiness while driving. Eye extraction, face extraction with dlib.

Introduction

Driver Drowsiness Detection is a crucial safety measure aimed at preventing fatigue-related accidents, which cause thousands of deaths and injuries annually. The project focuses on developing a system that continuously monitors a driver’s eyes to determine whether they are open or closed for extended periods, using video-based real-time detection.

Aim and Objectives:

• Develop a model to monitor drivers’ eye states in real-time.

• Detect signs of drowsiness through eye closure, yawning, and other facial behaviors.

• Reduce vehicle speed and manage traffic by alerting drivers before accidents occur.

Methodology:

• Uses cameras and facial landmark detection to extract eye regions from video frames.

• Eye Aspect Ratio (EAR) calculations track eye closure duration; prolonged closure triggers alerts.

• Yawning and other facial cues are also analyzed as indicators of fatigue.

• Experimental analysis uses simulated driving data (MIROS) to validate detection accuracy.

System Design:

• Includes block diagrams and flowcharts representing the process of capturing video, detecting faces, analyzing eye states, and triggering alerts.

Advantages:

• Enhances road safety and workplace safety.

• Provides early warning to prevent accidents.

• Real-time monitoring and user-friendly interface.

• Compatible with vehicle safety systems and customizable to driver behavior.

• Cost-effective by reducing accident-related costs.

Disadvantages / Limitations:

• Dependent on proper ambient lighting; poor light may reduce detection accuracy.

• Effective only within an optimal distance range from the camera (25–80 cm).

• Face orientation affects detection; extreme tilts may cause errors.

• Multiple faces or glasses may hinder accurate eye detection.

Conclusion

In conclusion, the development of a real-time drowsiness detection system using computer vision and facial landmarks represents a significant advancement in the field of road safety. Through the integration of innovative technologies and sophisticated algorithms , the proposed solution offers a proactive approach to mitigating the risks associated with drowsy driving. The importance of detecting drowsiness in real-time cannot be overstated, given its potential to prevent accidents, save lives, and enhance overall road safety. By continuously monitoring driver alertness and promptly alerting drivers to their drowsy state, the proposed system addresses critical gap in existing safety measures. The success of the project lies in its ability to leverage facial landmark analysis and eye blink patterns to accurately detect signs of drowsiness, even in challenging real-world conditions. Through rigorous experimentation and evaluation, the system has demonstrate edits effectiveness in accurately identifying drowsy drivers and triggering timely interventions. Looking ahead, future research and development efforts can focus on refining the system\'s algorithms, improving its robustness, and exploring additional features to enhance its

References

Research Papers: [1] You can find relevant academic papers by searching databases like Google Scholar or IEEE explore using key words like\" driver drowsiness detection system\" or \"drowsiness detection techniques\". These resources will provide in-depth studies on the topic. [2] \"https://www.researchgate.net/publication/370105178_DRIVER_DROWSINESS_DETECTION\"(DriverDrowsinessDetectionSystems–ResearchGate)offers a downloadable PDF discussing various drowsiness detection systems. Report on Drowsiness Detection System: [3] \"https://www.slideshare.net/vigneshwarvs/driver-drowsiness-detection\" (Driver Drowsiness Detection report | PDF – Slide Share) provides a presentation on driver drowsiness detection systems that you can reference for an over view of the topic. Bibliographies on Drowsiness Detection: [1] \"https://www.grafiati.com/en/literature-selections/driver-drowsiness-detection/\"(Bibliographies:\'DriverDrowsinessDetection\'-Grafiati) curates a list of relevant references on driver drowsiness detection, including journal articles, theses, and conference papers. Project Implementation Example: [2] While not a formal report, this YouTube video demonstrates a Drowsiness Detection System using OpenCV [YouTube driver drowsiness detection system using opencv .It can be a helpful resource to understand the practical implementation of the system.

Copyright

Copyright © 2025 Mahesh Kanjikar, Rupali Patil, Vaishnavi Patwari, Rakshita Limbave, Rajeshwari Patil. 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.