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

通过使用机器视觉和数字平台加快向循环经济的过渡，以实现更智能的表征、无处不在的跟踪和垃圾的自动分类。先前的研究开发了一种名为Recycleye的概念验证神经网络，它提供了一个负担得起的系统：识别和分类可回收物品，提高分类效率，减少人类的苦差事，并推动废物数据革命。该项目将回答以下问题：该系统在工业条件下或在更多可能分类的情况下表现如何？能否改进神经网络体系结构，例如，当看到披萨盒时，系统以100%的准确率识别出这是一个纸盒，尽管它可能被撕裂，被另一件物品半隐藏起来，并被泥土覆盖？使用无监督学习(AI)技术可以对废物流中的所有物品进行映射和分类吗？如果设备的成本可以降低，我们是否可以开始跟踪单个废物，以创建与供应链一样高效的清除链？在对数以千计的废品执行分类时，品牌级分类是否仍然准确，从而消除对条形码及其相关视线问题的需求？强化学习(AI)能否被用来创建机器人垃圾拾取器？这项技术能让延长的生产者责任计划走向个性化吗？这项工作将与几个工业合作伙伴一起完成，包括Biffa(材料回收设施运营商)、ByWaters(材料回收设施运营商)、Vito(比利时研究机构)和布鲁塞尔机场。

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.