This repository showcases a robust deep learning model designed for real-time fatigue detection using Convolutional Neural Networks (CNNs). The system relies on a connected camera positioned at the face level to provide timely and accurate results.
- Advanced Deep Learning Model: Our model is built on state-of-the-art CNN architecture to ensure high accuracy in detecting fatigue.
- Large and Diverse Dataset: The model was trained on a substantial dataset comprising hundreds of facial images meticulously labeled into two categories: normal and drowsy faces.
- Real-Time Capability: The system is optimized for real-time processing, making it suitable for applications where timely fatigue detection is crucial.
In addition to the technical files, this repository includes a comprehensive scientific and technical report. This report encapsulates over six months of dedicated work, outlining the primary scientific methods and technical procedures employed throughout the project. It serves as a valuable resource for understanding the intricacies of the model development and the rationale behind the chosen approaches.
Note: For optimal performance, ensure the camera is positioned at the face level during deployment.
Feel free to explore the repository to delve into the details of our fatigue detection system, and refer to the accompanying report for a deeper insight into the project's methodology and results.