Driver Drowsiness Detection

Real-time drowsiness detection on driver's face continuously for signs of fatigue using deep learning methodologies

AI & Machine LearningJupyter NotebookMIT

Abstract

Driver Drowsiness Detection is an open-source AI & Machine Learning project. Real-time drowsiness detection on driver's face continuously for signs of fatigue using deep learning methodologies. = Driver Drowsiness Detection System left 3 font rouge assets. It is built using Jupyter Notebook, Computer Vision, Deep Learning. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.

1. Introduction

= Driver Drowsiness Detection System left 3 font rouge assets

Driver fatigue represents a significant risk factor in vehicular accidents globally. This project implements a real-time Driver Drowsiness Detection system designed to mitigate this risk by leveraging advanced computer vision techniques and deep learning, specifically employing Siamese Neural Networks. The system performs continuous monitoring of the driver's facial region via a standard camera feed, analyzing key physiological indicators associated with drowsiness to provide timely alerts.

Effective training of the Siamese network necessitates a structured data preprocessing pipeline. This pipeline transforms raw image data into appropriately formatted pairs (anchor, positive, negative) for similarity learning.

2. Objective

Real-time drowsiness detection on driver's face continuously for signs of fatigue using deep learning methodologies

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

4. Technology Stack

Jupyter NotebookComputer VisionDeep Learning

5. System Requirements

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

  • Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
  • pip for dependencies
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/DivitMittal/Driver-Drowsiness-Detection.git
cd Driver-Drowsiness-Detection

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 DivitMittal 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.

View Source on GitHubDownload ZIP

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