Accelerating the transition towards a circular economy by using machine vision and digital platforms

Accelerating the transition towards a circular economy by using machine vision and digital platforms to enable smarter characterisation, ubiquitous tracking and automated sorting of waste.
Previous research has developed a proof of concept neural network called Recycleye which provides an affordable system to: identify and classify recyclables, improve sorting efficiency, reduce human drudgery and drive a waste data revolution. This project will answer the following questions: How would the system perform under industrial conditions or with a greater number of possible classifications? Can the neural network architecture be improved such that for example when seeing a pizza box, the system understands with 100% accuracy that it is a carton despite the fact it may be torn, half hidden by another item and covered in dirt? Could the use of unsupervised learning (AI) techniques enable the mapping and classification of all items within a waste stream? If the cost of the device can be reduced, can we start tracking individual waste items to create removal chains as efficient as supply chains? Will brand level classification remain accurate when performed on thousands of waste items, thereby removing the need for barcodes and their associated line of sight issues? Can reinforcement learning (AI) be used to create robotic waste pickers? Can the technology allow extended producer responsibility schemes to move towards individualization? The work will be completed with several industrial partners including Biffa (a material recovery facility operator), Bywaters (a material recovery facility operator), VITO (a Belgian research institution) and Brussels Airport.