This project demonstrates the use of YOLOv3, a state-of-the-art object detection model, in conjunction with OpenCV and Python to detect helmets and number plates within images or videos.

The system leverages the power of YOLOv3, a convolutional neural network (CNN) architecture known for its speed and accuracy, to identify and localize helmets and number plates within visual data. OpenCV, a popular computer vision library, is employed for image processing tasks and integration with the YOLOv3 model.

The system consists of four main components:

1. Real-time Detection: YOLOv3's efficiency enables near real-time processing of images and videos, making it suitable for applications requiring immediate detection.

2. Customizable Model: The YOLOv3 model can be trained on custom datasets to detect objects beyond helmets and number plates, adapting it to specific use cases.

3. Accuracy and Precision: YOLOv3 exhibits high accuracy and precision in object detection tasks, ensuring reliable identification of helmets and number plates.

4. Integration with OpenCV: Seamless integration with OpenCV facilitates image preprocessing, visualization, and other computer vision operations.

To use the system, follow these steps:

1. Clone the repository.
2. Create a virtual environment (venv or virtualenv) in the project directory.
3. Activate the virtual environment.
4. Install the required dependencies.
   • Run pip install -r requirements.txt.
5. Run the detect.py file to execute the system.
   • If you are using Python 3, you can run python detect.py.
6. Alternatively, you can run the helmet.ipynb notebook file in Jupyter Notebook/JupyterLab.
7. Prepare Data: Ensure you have a dataset containing images or videos with annotated helmets and number plates.
8. Train the Model (Optional): If you need to customize the model for your specific dataset, follow the provided training instructions.
9. Run the Detection System: Execute the Python script (e.g., detection.py) to process images or videos.
10. The script will display the detected helmets and number plates along with bounding boxes and labels.

Note: The system is provided in both .py and .ipynb file formats.

The system requires the following dependencies:

• OpenCV
• TensorFlow/Keras
• NumPy
• imutils

This project is licensed under the MIT License. See the LICENSE file for more details.

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