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，Agave Networks团队将开发一种计算机视觉算法，评估废铁的物理质量特征，以纳入其现有的国内黑色二次材料市场在线平台。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直接向铸造厂出售其库存，而无需中介。英国钢铁加工商和中小企业废料场使用低成本或现有的硬件来收集其材料的图像，他们的系统创建个人数字护照，以验证材料的位置，日期和存在时间，为英国铸造厂提供经过验证的采购工具，无需额外的中介机构。其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-他们提出的创新是开发一种计算机视觉和机器学习算法，该算法可以从回收的黑色金属批次中提取特征，实时图像包括：* 污染物的百分比 * 氧化和降解的百分比 * 零件尺寸 * 潜在的寿命终止源 * 近似的批量和重量目前，回收的含铁物质的利益相关者目前依赖于目视检查来进行材料验证以用于贸易目的，这容易导致结果的可变性、错误、延迟和滥用。其他分级技术如XRF和涡流磁选机既不适合也不高效，也不符合成本效益，因此在整个供应链中很少或根本没有使用。根据DEFRA 2021，英国钢铁铸造行业可以将国内回收钢的使用量从每年260万吨增加到350万吨。无需额外投资。然而，废料利益相关者之间的数据差异和不匹配，减少了英国国内废料产能的使用，出口更具吸引力，导致每年损失2.25亿英镑。该项目的重点是英国可持续材料和制造业的挑战-通过帮助英国钢铁制造和回收利用采用更具资源效率的解决方案和技术，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。*为闭环金属设计和开发更高效的商业模式 * 提供回收的黑色金属材料从原产地到成品的可追溯性。*为英国钢铁制造商和回收商减少范围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，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.