ObjectDetection-MobileNetSSD

Beginner-friendly real-time object detection project using Python, OpenCV, and the MobileNetSSD deep learning model. Detects common objects such as person, car, dog, bottle, etc., through a live webcam feed.

🔍 Real-Time Object Detection using MobileNetSSD + OpenCV

This project demonstrates real-time object detection using the MobileNetSSD deep learning model and OpenCV's DNN module.
It runs through a webcam feed and detects 20 common objects such as person, car, dog, cat, bottle, chair, and more.

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🚀 Features

• Real-time webcam object detection
• Powered by MobileNetSSD (SSD + MobileNet architecture)
• Detects 20 common object categories
• Beginner-friendly Python code
• Uses OpenCV’s high-speed DNN module
• Easy to run on any system (no GPU required)

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📁 Project Structure

ObjectDet/ │ ├── object.py # Main object detection script ├── deploy.prototxt # Model architecture ├── MobileNetSSD_deploy.caffemodel # Model weights (not included due to size) └── README.md # Documentation

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📥 Download Model Files (Required)

To run the project, download the MobileNetSSD model files:

• MobileNetSSD_deploy.caffemodel
  https://github.com/chuanqi305/MobileNet-SSD/raw/master/MobileNetSSD_deploy.caffemodel

• deploy.prototxt
  https://github.com/chuanqi305/MobileNet-SSD/raw/master/deploy.prototxt

Place both files in the project folder.

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🛠 Requirements

Install these Python packages:

pip install opencv-python
pip install numpy
▶️ How to Run

Clone the repository:

git clone https://github.com/your-username/ObjectDetection-MobileNetSSD.git


Navigate to the folder:

cd ObjectDetection-MobileNetSSD


Run the object detection script:

python object.py


Press Q to close the webcam window.

🧠 How It Works

Input frame from webcam → converted to a blob

Blob passed through SSD model → predictions generated

Bounding boxes + labels drawn on screen in real-time

📝 License

This project is open-source and free to use for learning and development.

🤝 Contributing

Pull requests are welcome!
If you have improvements or new ideas, feel free to contribute.

⭐ Support

If you found this project helpful, consider giving it a star ⭐ on GitHub!

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