Contrastive Learning on Industrial Recycling
Overview
Applied contrastive learning techniques to enhance material classification in industrial recycling processes. The project focused on improving the accuracy and efficiency of automated sorting systems using self-supervised learning approaches to distinguish between different recyclable materials.
Key Features
- Self-Supervised Learning: Contrastive learning framework that learns material representations without extensive labeled data
- Multi-Modal Classification: Integration of visual, spectral, and physical property data for enhanced accuracy
- Real-Time Processing: Optimized model for industrial-speed sorting operations
- Adaptive Learning: Continuous model improvement through feedback from sorting outcomes
- Contamination Detection: Identification of non-recyclable contaminants in material streams
Technical Implementation
Machine Learning: Custom contrastive learning architecture with ResNet backbone, trained on industrial recycling datasets with data augmentation strategies.
Data Processing: Multi-sensor data fusion combining RGB imaging, NIR spectroscopy, and physical measurements for comprehensive material characterization.
Deployment: Edge computing solution for real-time inference with sub-second classification times suitable for high-throughput industrial environments.
Results
Achieved 94% classification accuracy across 12 material categories, representing a 15% improvement over traditional supervised learning methods. The system demonstrated robust performance in varying lighting conditions and with contaminated materials, leading to increased recycling efficiency and reduced waste stream contamination.