https://github.com/afnanksalal/waste-detection

This project implements a waste classification system that uses a custom YOLO model for detecting and categorizing waste materials. It integrates with an Arduino to communicate the classification results.
https://github.com/afnanksalal/waste-detection

ai arduino classification object-detection opencv vision waste yolo

Last synced: 2 months ago
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This project implements a waste classification system that uses a custom YOLO model for detecting and categorizing waste materials. It integrates with an Arduino to communicate the classification results.

• Host: GitHub
• URL: https://github.com/afnanksalal/waste-detection
• Owner: Afnanksalal
• License: mit
• Created: 2024-10-17T14:14:16.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-10-22T15:39:35.000Z (8 months ago)
• Last Synced: 2025-04-04T07:44:02.384Z (2 months ago)
• Topics: ai, arduino, classification, object-detection, opencv, vision, waste, yolo
• Language: Python
• Homepage: https://www.linkedin.com/posts/afnanksalal_ai-machinelearning-deeplearning-activity-7252684227966578688-a5AJ?utm_source=share&utm_medium=member_desktop
• Size: 11.3 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Waste Classification System using YOLO and Arduino

This project implements a waste classification system that uses a custom YOLO model for detecting and categorizing waste materials. It integrates with an Arduino to communicate the classification results.

## Table of Contents

- [Features](#features)

- [Requirements](#requirements)

- [Installation](#installation)

- [Usage](#usage)

- [Results](#results)

- [Output Images](#output-images)

- [Contributing](#contributing)

- [License](#license)

## Features

- Real-time waste classification using a webcam and YOLO model.

- Categorizes items into burnable and non-burnable waste.

- Sends classification results to an Arduino for further processing.

- Web interface for streaming video and viewing detection results.

## Requirements

- Python 3.8 or higher

- See `requirements.txt` for a complete list of dependencies.

## Installation

1. Clone the repository:

   ```bash

   git clone https://github.com/Afnanksalal/customyolowastesorting.git

   cd repository-name

   ```

2. Create a virtual environment (optional but recommended):

   ```bash

   python -m venv venv

   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

   ```

3. Install the required packages:

   ```bash

   pip install -r requirements.txt

   ```

4. Place your YOLO model file (`best.pt`) in the project directory.

## Usage

1. Connect your Arduino to the computer and ensure the correct COM port is set in the code.

2. Run the FastAPI application:

   ```bash

   python main.py

   ```

3. Open your web browser and go to `http://localhost:8000` to access the web interface.

4. The webcam feed will display detected waste items, and the classification status will be sent to the Arduino.

## Output

![Output](images/output.jpg)

## Results

![Result](images/results.png)

*Result*

![Normalized Confusion Matrix](images/confusion_matrix_normalized.png)

*Normalized Confusion Matrix*

![PR Curve](images/PR_curve.png)

*PR Curve*

![P Curve](images/P_curve.png)

*P Curve*

![R Curve](images/R_curve.png)

*R Curve*

![F1 Curve](images/F1_curve.png)

*F1 Curve*

## Contributing

Contributions are welcome! Please open an issue or submit a pull request for any enhancements or bug fixes.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.