What is Trash Classification AI?
The Trash Classification AI System is an intelligent waste management solution that combines computer vision, deep learning, and robotics to automatically identify and sort different types of trash. Built with YOLOv11 and PyTorch, it provides real-time object detection and segmentation capabilities for waste classification.Problem Statement
Traditional waste sorting is manual, time-consuming, and error-prone. Recycling facilities need automated solutions that can:- Accurately identify different types of waste materials
- Process items in real-time at high throughput
- Integrate with robotic systems for physical sorting
- Adapt to different waste types through training
Solution Overview
This system addresses these challenges through:Computer Vision
YOLOv11-based segmentation model for accurate trash detection
Deep Learning
PyTorch-powered neural networks with hardware acceleration
Real-time Processing
Video stream analysis with object tracking and trajectory mapping
Robotics Integration
VEX arm controller coordination for automated physical sorting
System Capabilities
Three-Class Classification
The system classifies waste into three main categories:- Cardboard and Paper - Recyclable paper products
- Metal - Aluminum cans, steel containers, and metal objects
- Plastic - Plastic bottles, containers, and packaging
Hardware Acceleration
Optimized device management supports multiple hardware configurations:- CUDA - NVIDIA GPU acceleration for high-performance inference
- MPS - Apple Silicon Metal Performance Shaders for M1/M2 Macs
- CPU - Fallback CPU processing for systems without GPU support
Visual Annotations
Rich visualization features include:- Mask Drawing - Color-coded segmentation masks for each waste category
- Bounding Boxes - Detection boxes with class labels and confidence scores
- Object Tracking - Trajectory paths showing object movement across frames
Architecture Components
The system consists of four main modules:Segmentation Module
Segmentation Module
Handles YOLO model loading, device management, and inference. Performs real-time object detection and segmentation on video frames with confidence thresholding.
Drawing Module
Drawing Module
Provides visualization components for masks, bounding boxes, and tracking trajectories. Renders color-coded annotations on processed frames.
Processing Module
Processing Module
Coordinates the classification pipeline by orchestrating segmentation and drawing operations. Main entry point for frame-by-frame processing.
Robotics Module
Robotics Module
Manages VEX arm controller communication, sensor feedback, safety protocols, and scanning operations for physical waste sorting.
Use Cases
Educational Projects
Perfect for learning about:- Computer vision and object detection
- Deep learning model training
- Robotics and automation
- Environmental technology
Research Applications
Suitable for research in:- Waste management optimization
- Recycling automation
- Computer vision algorithms
- Human-robot interaction
Production Systems
Foundation for building:- Automated recycling facilities
- Smart waste bins
- Sorting conveyor systems
- Quality control stations
Technical Requirements
The system requires Python 3.8+, PyTorch 2.5.0+, and Ultralytics 8.3.22+ for core functionality. See the Installation Guide for complete setup instructions.
Minimum Hardware
- CPU: Multi-core processor (4+ cores recommended)
- RAM: 8GB minimum (16GB recommended)
- Storage: 5GB for models and dependencies
- Camera: USB webcam or video input source
Recommended Hardware
- GPU: NVIDIA GPU with 4GB+ VRAM (RTX 2060 or better)
- RAM: 16GB or more
- Camera: HD webcam (720p or higher)
- Robot: VEX IQ or VEX V5 system (optional)
Getting Help
GitHub Repository
View source code and report issues
API Reference
Explore detailed API documentation
Next Steps
Ready to get started? Follow our guides:- Installation - Set up your development environment
- Quickstart - Run your first classification
- Core Concepts - Understand the system design
- Training Guide - Train custom models