♻️ Plastic Object Detection using YOLO & Streamlit

A real-time and image-based Plastic Object Detection System built using YOLO (Ultralytics) and Streamlit.
This application detects plastic objects from uploaded images as well as through a live webcam feed, helping support environmental monitoring and waste management initiatives.

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🚀 Features

• 📷 Image Upload Detection

  • Upload JPG, JPEG, or PNG images
  • Automatic plastic object detection with bounding boxes

• 🎥 Real-Time Webcam Detection

  • Live plastic detection using your system camera
  • Adjustable confidence threshold

• ⚙️ Customizable Confidence Threshold

  • Control detection sensitivity from the sidebar

• 🎨 Modern UI

  • Dark-themed Streamlit interface
  • Clean layout and responsive design

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🧠 Tech Stack

• Python
• Streamlit – Frontend web interface
• YOLO (Ultralytics) – Object detection model
• OpenCV – Webcam and image processing
• NumPy
• Pillow (PIL)

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📂 Project Structure

├── app.py                  # Main Streamlit application
├── best.pt                 # Trained YOLO model (not included in repo)
├── requirements.txt        # Project dependencies
└── README.md               # Project documentation

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⚙️ Installation & Setup

1️⃣ Clone the Repository

git clone https://github.com/your-username/plastic-object-detection.git
cd plastic-object-detection

2️⃣ Create Virtual Environment (Recommended)

python -m venv venv
source venv/bin/activate      # On Linux/Mac
venv\Scripts\activate         # On Windows

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Add YOLO Model

Place your trained YOLO model file in the project root:

best.pt

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▶️ Run the Application

streamlit run app.py

The app will open automatically in your browser.

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🖥️ How It Works

1. Select detection mode from the sidebar:

   • Image Upload
   • Real-Time Webcam

2. Adjust the confidence threshold

3. View detected plastic objects with bounding boxes and labels

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🌍 Use Cases

• Environmental monitoring
• Smart waste management systems
• Recycling automation
• Research & academic projects
• AI-powered sustainability solutions

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⚠️ Notes

• Ensure your webcam is accessible for real-time mode
• best.pt must be compatible with Ultralytics YOLO
• GPU is recommended for better performance (optional)

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📜 License

This project is licensed under the MIT License. You are free to use, modify, and distribute this software.

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👤 Authors

Muhammad Zeeshan Islam

Co-Founder – Unicodrex | Technical Lead – Skill Sprint

Rana Ali Husnain

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⭐ Support

If you find this project useful:

• ⭐ Star the repository
• 🍴 Fork it
• 🧠 Contribute improvements

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🤝 Contributions

Contributions, issues, and feature requests are welcome!

Feel free to open a pull request or issue.