Abstract
Driver Drowsiness Detection is an open-source AI & Machine Learning project. Driver Drowsiness Detection System. Drowsiness detection is a safety technology that can prevent accidents that are caused by drivers who fell asleep while driving. This technology will alert the driver when drowsiness is detected. It is built using Python. 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
Drowsiness detection is a safety technology that can prevent accidents that are caused by drivers who fell asleep while driving. This technology will alert the driver when drowsiness is detected.
Sorry we were not able to upload dataset on GitHub due to size limitation But you can find the links to download the dataset from the following text files
2. Objective
Driver Drowsiness Detection System
This project demonstrates how Python can be applied to a real-world AI & Machine Learning problem.
4. Technology Stack
- matplotlib
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/rudrabarad/Driver-Drowsiness-Detection.git
cd Driver-Drowsiness-DetectionFull 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.
- What dataset does the project use and how was it pre-processed?
- Which algorithm / model architecture is used and why was it chosen over alternatives?
- How are training and testing data split, and how is overfitting avoided?
- Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
- How would you deploy this model for real users?
9. Source Code & License
This project is developed by rudrabarad 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.
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