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

利用机器视觉和数字平台加速向循环经济转型

• 批准号: 2244402
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2244402
• 关键词: Accelerating; transition; towards; circular; economy

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.

通过使用机器视觉和数字平台，加速向循环经济的过渡，实现更智能的特征描述、无处不在的跟踪和废物的自动分类。之前的研究已经开发出一种名为Recycleye的概念验证神经网络，它提供了一种负担得起的系统：识别和分类可回收物品，提高分类效率，减少人力劳动，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的分类下表现如何？神经网络架构是否可以得到改进，例如，当看到一个披萨盒时，系统可以100%准确地理解它是一个纸箱，尽管它可能被撕破了，一半被另一件物品遮住了，并且被污垢覆盖？是否可以使用无监督学习（AI）技术对废物流中的所有物品进行映射和分类？如果设备的成本可以降低，我们是否可以开始追踪单个废物项目，以创建像供应链一样高效的清除链？当对成千上万的废物进行分类时，品牌级别的分类是否仍然准确，从而消除了对条形码及其相关视线问题的需要？强化学习（AI）可以用来制造机器人拾荒者吗？这项技术能让扩大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。