Abstract
Driver Drowsiness Detection is an open-source AI & Machine Learning project. Driver Drowsiness Detection with YOLOv8 and Facial Features Combat driver fatigue with this deep learning-powered system that utilizes YOLOv8 to detect open and closed eyes, accurately assessing drowsiness levels. YOLOv8 Driver drowsiness detection Deep learning project Enhanced by algorithm based on Facial Features. It is built using Python, PyTorch. 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
YOLOv8 Driver drowsiness detection Deep learning project Enhanced by algorithm based on Facial Features
Receive driver's eye status data using a YOLOv8 model. If the eyes are closed for 70% of frames within a 4.5-second window, trigger a sound alarm. If the eyes are open for 0.5 seconds, turn off the alarm and consider it not drowsy.
2. Objective
Driver Drowsiness Detection with YOLOv8 and Facial Features Combat driver fatigue with this deep learning-powered system that utilizes YOLOv8 to detect open and closed eyes, accurately assessing drowsiness levels.
This project demonstrates how Python, PyTorch can be applied to a real-world AI & Machine Learning problem.
4. Technology Stack
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/hkimw/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 hkimw 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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