https://github.com/phenomsg/cv-based-waste-identifier

A computer vision-based waste identifier utilizes advanced image processing techniques and machine learning
https://github.com/phenomsg/cv-based-waste-identifier

binary binaryclassification cnn computer-vision deep-learning python3

Last synced: 10 days ago
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A computer vision-based waste identifier utilizes advanced image processing techniques and machine learning

• Host: GitHub
• URL: https://github.com/phenomsg/cv-based-waste-identifier
• Owner: PhenomSG
• Created: 2023-07-12T20:12:48.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-10-13T14:00:52.000Z (about 1 year ago)
• Last Synced: 2024-11-07T21:25:30.216Z (2 months ago)
• Topics: binary, binaryclassification, cnn, computer-vision, deep-learning, python3
• Language: Jupyter Notebook
• Homepage:
• Size: 21 MB
• Stars: 1
• Watchers: 2
• Forks: 4
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Computer Vision-based Waste Identifier 🌍♻️

An intelligent waste management system powered by computer vision to segregate recyclable and non-recyclable waste items.

## Overview 📝

The Computer Vision-based Waste Identifier is a project aimed at revolutionizing waste management practices through cutting-edge technology. By utilizing advanced image processing and machine learning, this project tackles the challenge of accurately segregating recyclable and non-recyclable waste items.

## Features 🌟

- **Automated Segregation:** Our system employs computer vision algorithms to automatically identify and classify waste items in real-time.

- **Recyclable vs. Non-recyclable:** It distinguishes between recyclable and non-recyclable waste, promoting efficient waste sorting.

- **Accurate Classification:** Through deep learning techniques, the system achieves high accuracy in waste item categorization.

- **User-Friendly Interface:** A user-friendly interface displays the segregation results and provides insights into waste management.

## How It Works 🤖📸

1. Cameras capture images of waste items.

2. Computer vision algorithms process the images and extract relevant features.

3. A trained model classifies the waste items as recyclable or non-recyclable.

4. Results are presented through the user interface.

## Future Prospects 🔮🌱

- **Enhanced Recycling:** Accurate waste segregation boosts the quality of recycled materials, contributing to a more efficient recycling process.

- **Environmental Impact:** Proper waste sorting reduces contamination and ensures proper disposal, minimizing environmental harm.

- **Smart Waste Management:** Integration with IoT devices and data analytics could lead to optimized waste collection routes and schedules.

- **Education and Awareness:** The system can be extended to raise awareness about waste classification and encourage responsible waste disposal.

## Get Involved! 🚀

Contributions, feedback, and ideas are welcomed! Let's work together to create a cleaner, more sustainable future. 🌎♻️

## Working

A computer vision-based waste identifier utilizes advanced image processing techniques and machine learning algorithms to accurately classify and sort waste. Its key aspects include:

**1. Image Capture:** Utilizing cameras or input devices to capture images of waste items.

**2. Preprocessing:** Enhancing image quality, removing noise, and standardizing the dataset.

**3. Feature Extraction:** Extracting relevant features from waste images for classification.

**4. Classification Model:** Training machine learning models to identify and categorize different types of waste.

**5. Real-time Identification:** Deploying the system to identify waste items in real-time, facilitating efficient waste management and recycling processes.