Driver Drowsiness Detection System

Abstract

1. Introduction

The Driver Drowsiness Detection System is a non-intrusive solution designed to monitor and detect signs of fatigue in drivers. By analyzing eye states through a camera feed, the system can identify early symptoms of drowsiness and issue timely warnings, helping to prevent accidents caused by driver fatigue. This project aims to enhance road safety, especially for those driving long distances who may not recognize their own drowsiness in time.

2. Objective

AI-powered Driver Drowsiness Detection System using Computer Vision & Machine Learning for real-time driver alertness monitoring and accident prevention.

This project demonstrates how Python, Computer Vision, Deep Learning can be applied to a real-world AI & Machine Learning problem.

3. Key Features / Modules

• Real-time monitoring of driver's eyes using a camera
• Fatigue and drowsiness detection algorithm
• Timely alerts to prevent the driver from falling asleep
• Non-intrusive and easy to set up
• Progressive Web App (PWA) support - Install like a native app!
• Offline functionality with caching
• Fast loading with optimized performance on System
• Real-time monitoring of driver’s eyes using a camera
• Fatigue and drowsiness detection algorithm
• Timely alerts to prevent the driver from falling asleep

4. Technology Stack

• Dlib or Mediapipe (for facial landmark detection)
• NumPy, Pandas

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/Gagandeep-2003/driver-drowsiness-detection-system.git
cd driver-drowsiness-detection-system

1. Clone the repository:
2. Install dependencies:
3. Download the model:
4. Download Link: shape_predictor_68_face_landmarks.dat
5. Run the application:
6. Ensure your webcam is connected.
7. Run the application as shown above.
8. The system will start monitoring your eyes and alert you if signs of drowsiness are detected.

git clone https://github.com/Gagandeep-2003/Driver-Drowsiness-Detection-System.git
   cd Driver-Drowsiness-Detection-System

pip install -r requirements.txt

python main.py

# Standard web app
streamlit run streamlit_app/streamlit_app.py

# Or run the enhanced PWA version
streamlit run streamlit_app/streamlit_app_pwa.py

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by Gagandeep-2003 and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.