Deep Learning for Fly-tipped Waste Detection

Deep Learning for Fly-tipped Waste Detection

Principal Investigators: Florian Urmetzer and Tyler Holderness, University of Cambridge
Council Partners: South Cambridgeshire District Council  and  Greater Cambridge Shared Waste Services

Fly-tipping places a significant financial and environmental burden on local authorities, with over 1.15 million incidents recorded across England in 2023/24 alone. This project will develop a deep learning computer vision pipeline that leverages cameras already fitted to Refuse Collection Vehicles to detect fly-tipped waste during routine rounds. The system will automatically extract the location, timestamp, waste description, and photographic evidence needed to generate a report, with human-in-the-loop verification before any action is taken. By converting an existing but underutilised data asset into a proactive detection tool, the project aims to reduce the reporting burden on the public and accelerate the resolution of incidents.