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https://github.com/docsallover/helmet-and-plate-detection

Helmet and Number Plate Detection using YOLOv3 with opencv and python
https://github.com/docsallover/helmet-and-plate-detection

data-science detection jupyter-notebook machine-learning numpy opencv python tenserflow yolo yolov3

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Helmet and Number Plate Detection using YOLOv3 with opencv and python

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# Helmet and Number Plate Detection using YOLOv3 with OpenCV and Python



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.



## Overview

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.



## How to Use

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.



## Dependencies

The system requires the following dependencies:

- OpenCV

- TensorFlow/Keras

- NumPy

- imutils



## License

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



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