This system enhances road safety by detecting and alerting drivers when they show signs of drowsiness.
- CNN: Run
DDDS_CNN/model_training.py
Follow the instructions in the notebooks to evaluate the accuracy of both models.
- CNN: Execute
DDDS_CNN/main_capture.py
The project uses Convolutional Neural Networks (CNN)
Required libraries:
- keras
- cv2
- pygame
- numpy
- matplotlib
- glob
- tqdm
Datasets are available from specific sources detailed in the repository, with guidelines for accessing and preparing them for training.
Feel free to contribute or raise issues. Check the project documentation for more details.
The project is available under the MIT license, promoting open and free use, modification, and distribution.
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>
inmodel_training.py
with the paths to your training and testing dataset directories, respectively.<PATH_TO_SAVE_MODEL>
intraining_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>
indetection_script.py
with the respective paths to the alarm sound file, Haar cascade XML files, and the saved CNN model.