Plastic packaging, a complete recognition and monitoring system based on AI and fusing RGB-based computer vision with Near Infrared spectral Imaging (NIR SI)

塑料包装，基于人工智能并将基于 RGB 的计算机视觉与近红外光谱成像 (NIR SI) 融合的完整识别和监控系统

• 批准号: 10020954
• 金额: $ 43.54万
• 依托单位: GREYPARROT.AI LTD
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10020954
• 关键词: Plastic; packaging; complete; recognition; monitoring

By 2040, c.3billion tons of waste will be produced globally, with plastic waste doubling to 430million tons. It is predicted that only 13% will be recycled due to inefficient, inaccurate (manual) auditing and processing, driven by a lack of data, thus inhibiting the effective sorting of waste. Sorters using Near InfraRed (NIR) cameras, in place today, provide a partial solution; however they are extremely expensive (£200k-£300k/unit), and technically limited (NIR vision is unable to differentiate all types of waste), meaning plastics packaging recovery/recycling rates are significantly lower than current UK Plastics Pact targets (c.50% in 2019, WRAP-2020 vs. 70%-target).To address the challenge, Greyparrot (GP) in collaboration with Blue Green Vision (BGV) are developing a hybrid waste recognition and monitoring system to complement and enhance NIR sorting machines, and tackle the lack of accurate data with a holistic approach. The system will combine Greyparrot's AI-based technology using RGB cameras (Red-Green-Blue i.e.human vision) with BGV's high-speed point-to-point NIR system (5-10x cheaper than current hyperspectral NIR cameras) to provide automated, cost-effective, accurate recognition of plastics waste (with transferability to all waste types) at-scale. Critically, the system will enable the identification of all types of material and packaging, e.g including black plastics or Polyethylene terephthalate (PET) bottles with a Low-Density Polyethylene (LDPE) sleeve, not currently recognised/sorted by existing NIR systems. By providing accurate and real-time data required to improve processes in the plant, and integrating the proposed system to enhance sorting machines, the partners anticipate a conservative 5% increase in plastics recovery rates; making a significant contribution towards delivering the 3rd target of the UK Plastics Pact.The unique waste recognition and monitoring system will enable waste companies to accurately and comprehensively audit waste at-scale, critical to meeting strict Extended Producer Responsibility (EPR) legislation set to take effect in 2023,(with which samples will be required as often as every 8 tonnes against every 125 tonnes currently) providing greater accountability and increased recycling rates. This project will be a catalyst to deliver such a service. In addition, the system will support new approaches to carry out a global optimisation of entire plants instead of local optimisations in singular sorters, leading to better recovery rates in all waste categories and helping to build a stronger UK recycling system.

到2040年，全球将产生约30亿吨废物，其中塑料废物将翻一番，达到4.3亿吨。据预测，只有13%将被回收，这是由于缺乏数据导致的低效、不准确（人工）审计和处理，从而抑制了废物的有效分类。目前，使用近红外（NIR）相机的分拣机提供了部分解决方案;然而它们非常昂贵（20万英镑-30万英镑/台），技术上有限（近红外视觉无法区分所有类型的废物），这意味着塑料包装回收/再循环率明显低于当前英国塑料公约的目标为应对这一挑战，Greyparrot（GP）与Blue绿色Vision（BGV）合作开发了一种混合废物识别和监测系统，以补充和增强近红外分类机，并以全面的方法解决缺乏准确数据的问题。该系统将结合联合收割机Greyparrot的基于人工智能的技术，使用RGB相机（红绿蓝，即人类视觉）与BGV的高速点对点近红外系统（比目前的高光谱近红外相机便宜5- 10倍），以提供自动化，具有成本效益，大规模准确识别塑料废物（可转移到所有废物类型）。重要的是，该系统将能够识别所有类型的材料和包装，例如，包括黑色塑料或带有低密度聚乙烯（LDPE）套管的聚对苯二甲酸乙二醇酯（PET）瓶，目前无法通过现有的近红外系统识别/分类。通过提供改善工厂流程所需的准确和实时数据，并集成拟议的系统以增强分拣机，合作伙伴预计塑料回收率保守增长5%;为实现英国塑料公约的第三个目标做出重大贡献。独特的废物识别和监控系统将使废物公司能够准确、全面地大规模审计废物，这对于满足将于2023年生效的严格的生产者延伸责任（EPR）立法至关重要（目前每125吨样品需要每8吨样品），提供更大的问责制和更高的回收率。该项目将成为提供这种服务的催化剂。此外，该系统还将支持新的方法，对整个工厂进行全球优化，而不是对单个分拣机进行局部优化，从而提高所有废物类别的回收率，并帮助建立更强大的英国回收系统。