The metal recycling industry faces a critical challenge: "unwanted items" like lithium-ion batteries and pressurized cylinders can cause catastrophic fires or damage expensive shredders. Manual sorting is often insufficient to catch these hazardous contaminants at scale. By leveraging CircularNet, Google Cloud's AI infrastructure, and Google Maps geospatial intelligence, recyclers can build a robust, predictive system to ensure safety and improve material purity. 1.The Vision

This guide outlines the technical roadmap for building an AI-powered system to identify unwanted contaminants in metal recycling streams. By combining Google's CircularNet models with the infrastructure of Google Cloud and the spatial intelligence of Google Maps, recycling facilities can transform manual sorting into a high-precision, data-driven operation. 1. The Core Engine: Understanding CircularNet CircularNet is an open-source machine learning model developed by Google s