Classification Architecture
The classification engine uses a fine-tuned convolutional neural network trained on a curated dataset of waste imagery spanning six primary material categories: plastics, metals, paper and cardboard, glass, organic matter, and general landfill waste. The model achieves high accuracy across these categories under varied lighting conditions and occlusion scenarios typical of real-world bin environments.
Edge inference runs on compact GPU-accelerated hardware mounted at bin locations, enabling sub-second classification without requiring cloud connectivity for every frame. Only classification metadata and aggregated statistics are transmitted upstream, minimizing bandwidth requirements while maintaining a complete audit trail of material flow.
When contamination is detected in a recycling stream, such as food waste deposited in a plastics bin, the system triggers an immediate alert to facility operators and logs the event for trend analysis. Over time, contamination pattern data informs targeted education campaigns and signage improvements at high-contamination locations.
