anikethhebbar / drowsy-detect

Driver Drowsiness Detection System

This system enhances road safety by detecting and alerting drivers when they show signs of drowsiness.

Getting Started

1. Clone the Repository

2. Model Training

• CNN: Run DDDS_CNN/model_training.py

3. Model Evaluation

Follow the instructions in the notebooks to evaluate the accuracy of both models.

4. Using the Models

• CNN: Execute DDDS_CNN/main_capture.py

System Overview

The project uses Convolutional Neural Networks (CNN)

Prerequisites

Required libraries:

• keras
• cv2
• pygame
• numpy
• matplotlib
• glob
• tqdm

Data Collection

Datasets are available from specific sources detailed in the repository, with guidelines for accessing and preparing them for training.

Support and Contributions

Feel free to contribute or raise issues. Check the project documentation for more details.

License

The project is available under the MIT license, promoting open and free use, modification, and distribution.

Path Configuration

After cloning the repository, update the following placeholders in the code with your actual file paths:

• <PATH_TO_TRAIN_DATASET> and <PATH_TO_TEST_DATASET> in model_training.py with the paths to your training and testing dataset directories, respectively.
• <PATH_TO_SAVE_MODEL> in training_script.py with the path where you want to save the trained model.
• <PATH_TO_ALARM_FILE>, <PATH_TO_HAAR_CASCADE>, and <PATH_TO_SAVED_MODEL> in detection_script.py with the respective paths to the alarm sound file, Haar cascade XML files, and the saved CNN model.