Welcome to Real-time Object Detection! This project utilizes the power of machine learning to detect objects in real-time using a pre-trained SSD MobileNet V3 model. With this project, you can easily identify various objects, such as people, vehicles, animals, and more, directly from your webcam feed.
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Model Initialization: The project loads a pre-trained SSD MobileNet V3 model, which has been trained on the COCO dataset for object detection.
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Object Detection: Using the model, the project performs real-time object detection on the frames captured from the webcam feed.
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Display Results: Detected objects are highlighted with bounding boxes and labeled with their corresponding class names in real-time, providing instant feedback.
To run Real-time Object Detection on your machine, follow these simple steps:
- Clone the Repository: Use the following command to clone the repository to your local machine:
git clone https://github.com/KelvinPuyam/Real-time-object-detection- Navigate to the Project Directory: Change your current directory to the cloned repository:
cd Real-time-object-detection- Run the Script: Execute the
real_time_object_detection.pyscript using Python:
python real_time_object_detection.py- Enjoy Real-time Object Detection: Once the script is running, your webcam will activate, and you'll see objects being detected in real-time on your screen. Press 'q' to quit the application and you will find a video recording of your detection session saved in the project directory. Happy detecting! 🎥🔍
- Python 3
- OpenCV
Contributions are welcome! Fork the repository, make changes in a new branch, and submit a pull request (PR). Provide a clear description of your changes. PRs will be reviewed by maintainers before merging. Feel free to open an issue for suggestions or questions. Thank you for contributing! 🚀
