Abstract
Real Time Driver Drowsiness Detection System Using Deep Learning is an open-source AI & Machine Learning project. CS-GY 6953 Deep Learning Major Project. Drowsiness detection systems have become increasingly important in preventing road accidents caused by driver fatigue. With long drives, it is common for drivers to become drowsy and even fall asleep behind the wheel. It is built using Jupyter Notebook, Computer Vision, Deep Learning, Keras. 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 systems have become increasingly important in preventing road accidents caused by driver fatigue. With long drives, it is common for drivers to become drowsy and even fall asleep behind the wheel. In this article, we will explore the development of a drowsiness detection system that can promptly alert the driver when signs of drowsiness are detected.
Drowsiness is identified by using vision-based techniques like eyes detection, yawning, and nodding. When it comes to yawning and nodding some people can sleep without yawning and nodding.
●OpenCV: OpenCV is a great tool for image processing and performing many computer vision tasks. It is an open-source library that can be used to perform tasks like face detection, object tracking, and many more tasks.
2. Objective
CS-GY 6953 Deep Learning Major Project
This project demonstrates how Jupyter Notebook, Computer Vision, Deep Learning can be applied to a real-world AI & Machine Learning problem.
4. Technology Stack
- requests
- cv2 (OpenCV)
- opendatasets
- matplotlib
- tensorflow
- tabulate
- playsound
- google.colab
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/Pratham-mehta/Real-time-Driver-Drowsiness-Detection-System-Using-Deep-Learning.git
cd Real-time-Driver-Drowsiness-Detection-System-Using-Deep-LearningFull 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 Pratham-mehta 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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