Developing computer vision algorithms for ferrous scrap and secondary materials grading using images and videos as input, incorporating them into digital passports for increasing UK usage of domestically generated scrap.

使用图像和视频作为输入，开发用于黑色金属废料和二次材料分级的计算机视觉算法，并将其纳入数字护照中，以增加英国对国内产生的废料的使用。

• 批准号: 10076415
• 金额: $ 6.26万
• 依托单位: AGAVE NETWORKS LIMITED
• 依托单位国家: 英国
• 项目类别: Grant for R&D
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10076415
• 关键词: Developing; computer; vision; algorithms; ferrous

During the six-month project, the Agave Networks team will develop a computer vision algorithm assessing ferrous scrap's physical quality characteristics to incorporate into their existing online platform for the ferrous secondary materials domestic market. They create and use digital passports using real-time images of the materials to enable UK SME to scrap generators and collectors to market their stock directly to foundries without intermediaries.UK steel processors and SME scrap yards use low-cost or existing hardware to gather images of their materials and their system creates individual digital passports to validate the material's location, date, and time of existence, providing UK foundries with a verified procurement tool, eliminating the need for additional intermediaries.Their easy-to-use and efficient solution helps close the recycling loop and trace UK-recovered metal materials from their origin to the furnace.Their proposed innovation is developing a computer vision and machine learning algorithm that extracts characteristics of recovered ferrous metal batches from real-time images including:* % of contaminant materials* % oxidation and degradation* Pieces dimensions* Potential end-of-life sources* Approximate batch volume and weightCurrently, ferrous recovered stakeholders currently rely on visual inspection for materials verification for trading purposes, which is prone to results' variability, error, delays and abuse. Other grading technologies like XRF and Eddy current magnetic separators are neither suitable nor efficient and non-cost effective for mixed ferrous materials; hence are little or not utilized across the supply chain.According to DEFRA 2021, the UK steel foundry industry can increase domestic recovered steel usage from 2.6 to 3.5 million tonnes p.a. without additional investment. However, data disparities and mismatches between scrap stakeholders, decreasing UK domestic scrap capacity usage and being exporting more attractive, translated into £225M in losses p.a. in addition to avoidable CO2 scope 2 and 3 emissions.This project focuses on the challenge of Sustainable UK Materials and Manufacturing - By helping the UK steel manufacturing and recycling to adopt more resource-efficient solutions and technologies, including artificial intelligence, computer vision and data communication platforms to:* Create more resilient procurement supply chains for recovered ferrous materials.* Design and develop a more efficient business model for closed-loop metals* Provide traceability of recovered ferrous materials from origin to finished product.* Reduce CO2 equivalent emissions on scopes 2 and 3 for UK steel manufacturers and recyclers.* Increase the international competitiveness of UK-manufactured steel by increasing the recycled content of traceable recovered materials.

在为期六个月的项目中，Agave Networks 团队将开发一种计算机视觉算法，评估黑色金属废料的物理质量特征，并将其纳入其现有的黑色金属二次材料国内市场在线平台中。他们使用实时图像创建和使用数字护照。这些材料使英国中小企业能够报废发电机和收集器，无需中间商即可将其库存直接销售给铸造厂。英国钢铁加工商和中小企业废品场使用低成本或现有硬件来收集其材料的图像，他们的系统创建个人数字护照验证材料的位置、日期和存在时间，为英国铸造厂提供经过验证的采购工具，消除对额外中介机构的需求。他们易于使用且高效的解决方案有助于关闭回收循环并追踪英国回收的金属材料他们提出的创新是开发一种计算机视觉和机器学习算法，从实时图像中提取回收的黑色金属批次的特征，包括：* 污染物材料百分比* 氧化和降解百分比* 碎片尺寸* 潜在的报废来源* 大约批次体积和重量 目前，黑色金属回收利益相关者目前依靠目视检查来进行材料验证以用于交易目的，这很容易出现结果变化、错误、延迟和滥用等其他分级技术。涡流磁选机既不适合混合黑色金属材料，也不高效且不具有成本效益；因此在整个供应链中很少或没有使用。根据 DEFRA 2021，英国钢铁铸造行业可以在不增加投资的情况下，将国内回收钢材的使用量从每年 2.6 万吨增加到 350 万吨。然而，废钢利益相关者之间的数据差异和不匹配，导致英国国内废钢产能使用量下降以及出口更具吸引力，除了可避免的二氧化碳排放之外，还带来了每年 2.25 亿英镑的损失。范围 2 和 3 排放。该项目重点关注可持续英国材料和制造的挑战 - 通过帮助英国钢铁制造和回收采用更资源有效的解决方案和技术，包括人工智能、计算机视觉和数据通信平台，以：* 为回收黑色金属材料创建更有弹性的采购供应链。* 设计和开发更高效的闭环金属商业模式* 提供回收黑色金属材料从原产地到成品的可追溯性。* 减少二氧化碳当量排放英国钢铁制造商和回收商的范围 2 和 3。* 通过增加可追溯回收材料的回收含量，提高英国制造的钢铁的国际竞争力。