Smart Dustbin Project

Overview

This repository contains the source code for a Smart Dustbin project that utilizes computer vision, machine learning, and IoT technologies to automatically segregate waste into biodegradable and non-biodegradable categories. The project involves two main components:

1. Object Detection and Classification: A computer with a camera captures live video of the waste being disposed of. The video feed is processed using TensorFlow to detect and classify objects as either biodegradable or non-biodegradable.

2. IoT Integration: An ultrasonic sensor is installed inside the dustbin to monitor the fill level in real-time. This data is transmitted to a Firebase Realtime Database, allowing users to track the waste level through a mobile application. Additionally, users can request waste pickup through the app.

Technologies Used

• TensorFlow/Keras for object detection and classification.
• OpenCV for image processing.
• Firebase Realtime Database for storing and retrieving data.
• Arduino for interfacing with sensors and controlling LED indicators.
• Python for running the main processing script.

Repository Structure

• dustbin_model/: Contains the TensorFlow/Keras model for object detection and classification.
• arduino_code/: Arduino sketch for interfacing with the ultrasonic sensor and controlling LED indicators.
• flutter_apps/: Flutter projects for the user-facing mobile applications.
• README.md: You are here! This document provides an overview of the project and instructions for setup.

Setup Instructions

1. Computer Setup:

   • Install necessary Python packages: numpy, opencv-python, tensorflow, firebase-admin, pyttsx3.
   • Ensure the computer is connected to the internet for Firebase integration.
   • Run the main Python script to start the object detection process.

2. Arduino Setup:

   • Upload the arduino_code/arduino_code.ino sketch to the Arduino board.
   • Connect the ultrasonic sensor and LED indicators as per the wiring diagram.

3. Firebase Configuration:

   • Create a Firebase project and set up a Realtime Database.
   • Generate a service account key and download the JSON file. Place it in the appropriate directory.
   • Update the Firebase database URL in the Python script accordingly.

4. Flutter App Setup:

   • Navigate to the flutter_apps/ directory and open each Flutter project in an IDE.
   • Modify the Firebase configuration to match your project's settings.
   • Build and deploy the apps to your Android/iOS devices.

Circuit Diagram

Usage

• Object Detection: The computer continuously processes live video from the camera. Detected objects are classified as biodegradable or non-biodegradable, and the results are displayed in real-time.
• Waste Level Monitoring: The ultrasonic sensor inside the dustbin measures the fill level, which is updated in the Firebase Realtime Database. Users can track the waste level through the mobile application.
• Waste Pickup Requests: Users can submit waste pickup requests through the mobile app. These requests are stored in the database and can be viewed by waste collection teams.

Acknowledgments

• Mention any libraries, resources, or tutorials used.
• Acknowledge any collaborators or contributors who helped with the project.