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.

在为期六个月的项目中，阿吉德网络公司的团队将开发一种评估黑色金属废料物理质量特征的计算机视觉算法，以纳入他们现有的面向黑色金属二次材料国内市场的在线平台。他们使用材料的实时图像创建和使用数字护照，使英国中小企业能够在没有中介的情况下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厂使用低成本或现有的硬件来收集材料的图像，他们的系统创建单独的数字护照来验证材料的位置、日期和存在时间，为英国铸造厂提供经过验证的采购工具。他们建议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：*%的污染材料*%的氧化和降解*碎片尺寸*潜在的寿命结束来源*近似的批次体积和重量目前，黑色金属回收的利益相关者目前依赖目视检查进行材料验证以进行交易，这容易导致结果的变异性、错误、延迟和滥用。其他分级技术，如XRF和涡流磁选机，对于混合黑色金属材料既不适合也不高效，也不具成本效益；因此在整个供应链中很少或没有得到利用。根据DEFRA 2021，英国钢铁铸造业可以将国内回收钢的使用量从260万吨/年提高到350万吨/年。不需要额外的投资。然而，废品利益相关者之间的数据差异和不匹配、英国国内废品产能使用量的减少以及出口的更具吸引力，转化为每年2.25亿GB的损失。除了可避免的二氧化碳排放范围2和3之外。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多资源高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：*为回收的黑色金属材料创建更具弹性的采购供应链。*设计和开发更有效的闭环金属商业模式*为回收的黑色金属材料提供从来源到成品的可追溯性。*减少英国钢铁制造商和回收商在范围2和3的二氧化碳当量排放。*通过增加可追溯回收材料的回收含量来提高英国制造的钢铁的国际竞争力。

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.