🚗 Driver Drowsiness Detection System 🛣️

Overview

The Driver Drowsiness Detection System is a cutting-edge solution aimed at enhancing road safety by monitoring driver fatigue in real-time. Leveraging advanced deep learning techniques, the system continually analyzes eye movements to detect signs of drowsiness and issues an alert to help prevent accidents.

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✨ Key Features

• Real-time Monitoring: 🔄
  The system continuously captures and processes video feed from the webcam to monitor eye movements.

• Deep Learning Model: 🧠
  A powerful Convolutional Neural Network (CNN) is trained to distinguish between open and closed eye states, enabling precise detection of drowsiness.

• Alert Mechanism: 🔔
  If drowsiness is detected, the system triggers an audible alarm, ensuring the driver remains alert and focused.

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📁 Project Structure

Driver-Drowsiness-Detection-System/
├── Annotation/                     # Contains annotation files for the dataset
├── MRL Eye Dataset/                # Directory for the MRL Eye Dataset
│   └── mrlEyes_2018_01/            # Subdirectory with eye images
├── models/                         # Directory to save trained models
├── prepared data/                  # Processed data ready for training
├── Data Preparation.ipynb          # Notebook for data preprocessing
├── Model Training.ipynb            # Notebook for model training
├── main.ipynb                      # Main application notebook
├── alarm.wav                       # Audio file for alarm
├── best_model.h5                   # Best trained model file
├── haarcascade_frontalface_alt.xml # Haar Cascade for frontal face detection
├── haarcascade_lefteye_2splits.xml # Haar Cascade for left eye detection
├── haarcascade_righteye_2splits.xml# Haar Cascade for right eye detection
├── README.md                       # Project documentation
└── requirements.txt                # List of required Python packages

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⚙️ Installation Guide

Prerequisites:

• Python 3.7+
• Webcam for real-time monitoring

Setup Instructions:

1. Clone the Repository:

   git clone https://github.com/Shubham-Singla259/Driver-Drowsiness-Detection-System.git
   cd Driver-Drowsiness-Detection-System

2. Install Required Packages: Ensure you have pip installed, and then run:

   pip install -r requirements.txt

3. Download the MRL Eye Dataset:

   • Download the dataset from the MRL Eye Dataset page on Kaggle.
   • Extract the dataset and place the mrlEyes_2018_01 folder inside the MRL Eye Dataset directory.

4. Run the Application: Open and run the main.ipynb notebook in Jupyter Notebook or Jupyter Lab. The notebook will connect to your webcam, process the live video feed, and alert you if drowsiness is detected.

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💻 Usage

• Data Preparation: Use the Data Preparation.ipynb notebook to preprocess the MRL Eye Dataset images, resize them, normalize the pixel values, and split the data into training and testing sets.

• Model Training: The Model Training.ipynb notebook will guide you through training the CNN model on the processed data. After training, the model with the best performance is saved as best_model.h5.

• Real-time Detection: Run the main.ipynb notebook to start real-time drowsiness detection using your trained model. Ensure that your webcam is properly connected.

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🤝 Contributing

We welcome contributions from the community! If you have suggestions for new features, improvements, or enhancements, please feel free to open an issue or submit a pull request.

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📜 License

This project is licensed under the MIT License. Please refer to the LICENSE file for more information.

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🙏 Acknowledgements

• MRL Eye Dataset: Special thanks to the MRL Lab at VSB-TUO for providing the dataset used in this project.

• Haar Cascade Classifiers: The Haar Cascade classifiers used for face and eye detection are part of the OpenCV library.

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Stay Safe. Stay Alert. 🚗💤