Waste Notification System
Developed a web application to automatically detect and classify different waste materials using Deep Learning and notify nearby municipal authorities for efficient waste management. The system enhances waste segregation and contributes to sustainable urban cleanliness.
🎯 Key Achievement
Achieved 70% accuracy in predicting waste types from user-uploaded images using TensorFlow-based Transfer Learning models.
Poor waste management leads to unhygienic conditions and environmental pollution. Manual segregation is inefficient and error-prone. This system aims to automate the detection and categorization of waste for better management.
🧠 Deep Learning-Based Classification
- Used TensorFlow and Transfer Learning (MobileNet) for waste classification
- Trained on a dataset containing multiple waste categories (plastic, organic, metal, etc.)
- Implemented image preprocessing for noise reduction and resizing
- Deployed trained model through a user-friendly web interface
🌐 Web Integration
- Developed using HTML, CSS, JavaScript, and Bootstrap
- Flask backend for model inference and API serving
- Notification system to alert nearby municipal authorities upon detection
- Responsive interface for desktop and mobile users
Python
TensorFlow
Transfer Learning
Flask
HTML
JavaScript
Bootstrap
Deep Learning
Computer Vision
- Automated Detection: Classifies waste type from uploaded image
- Smart Notifications: Sends alerts to municipal authorities
- Cloud Deployment: Flask-based backend for scalable access
- User-Friendly UI: Built with responsive design principles
Enabled faster and more accurate waste classification, promoting cleaner cities and reducing manual labor. The project demonstrates how AI can support smart city initiatives for sustainable waste management.