Garbage Detection and Sorting System

Table of Contents

1. Introduction
2. Features
3. Installation
4. Usage
5. Dataset
6. Demo
7. Target Audience
8. Contributions

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1. Introduction

This project implements an AI-powered garbage detection and sorting system using the YOLOv8 model for precise waste classification. The system is designed to enhance waste sorting efficiency and promote sustainable waste management practices.

2. Features

• Real-time garbage detection and classification
• Supports six categories: cardboard, plastic, glass, paper, metal, and biodegradable waste
• User-friendly interface using Streamlit
• Easy integration with camera systems for live detection
• Scalable and adaptable for various environments

3. Installation

1. Clone the repository:

   git clone https://github.com/binguliki/Garbage-Detection.git
   cd Garbage Detection

2. Install the required dependencies:

   pip install -r requirements.txt

4. Usage

1. Start the Streamlit application:

   streamlit run app.py

2. Click the "Start Camera" button to begin the live detection.

3. The system will display detected objects and their classifications in real-time.

4. Click the "Stop Camera" button to stop the live feed.

5. Dataset

• Description: The dataset includes images of various waste types, categorized into cardboard, plastic, glass, paper, metal, and biodegradable waste.
• Source: Waste Sorting Dataset (Replace with actual source)
• Preprocessing: Images are resized, normalized, and augmented for better training performance.

6. Demo

Garbage.Detection.sample.mp4

7. Target Audience

• Municipal Waste Authorities: Streamline waste sorting processes, reduce contamination, and improve overall efficiency.
• Recycling Facilities: Automate sorting, enhance recycling rates, and separate materials more effectively.
• Large Businesses: Improve waste management practices, minimize waste generation, and optimize recycling efforts.

8. Contributions

We welcome contributions to improve the project! To contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes.
4. Commit your changes (git commit -m 'Add new feature').
5. Push to the branch (git push origin feature-branch).
6. Create a pull request.

9. Contributors

Thanks to the following people who have contributed to this project:

• SurajPhani-github