Unlocking the potential of Computer Vision (CV) for SMEs -- Development of an AI platform enabling non-technical users to build advanced CV applications

释放中小企业计算机视觉 (CV) 的潜力——开发人工智能平台，使非技术用户能够构建先进的 CV 应用程序

• 批准号: 10033899
• 金额: $ 49.55万
• 依托单位: METALYNX 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=10033899
• 关键词: Unlocking; potential; Computer; Vision; CV

**Unlocking the potential of Computer Vision for SMEs - Development of an AI platform enabling non-technical users to build advanced CV applications**There is a significant amount of useful information and value locked in visual images, however the high cost of computer vision expertise and the time it takes to analyse and curate the data, means it's currently impossible for UK SMEs to fully realise its potential.From helping doctors diagnose medical conditions, to detecting crop disease by drones, to ensuring safety protocols on construction sites, computer vision has the potential to unlock new growth and efficiency opportunities across a wide variety of sectors.Unfortunately SMEs rarely have the CV expertise required, and the cost to hire/contract this expertise can be prohibitively expensive. Even then highly-skilled CV scientists are spending ~80% of their time on data curation alone\[3\], with their lack of domain expertise then leading to underperforming, unusable models\[4,5\]. Such challenges result in 55% of projects never making it to production\[3\].To increase adoption, solutions need to speed-up data curation processes, reduce curation costs and become more accessible to those without technical training. Organisations such as hospitals and manufacturers own both vast amounts of visual data and the domain expertise to interpret it, making them ideally placed to create impactful CV applications. Empowering them to build their own application can help accelerate AI-adoption and the resulting benefits.Collaborating with medical imaging experts at King's College London and retinal imaging SME Optos, Metalynx aims to significantly increase CV adoption amongst UK SMEs by removing the key barriers to adoption -- cost and expertise -- through the development of accessible tools to build advanced CV applications, easily.The ultimate aim of the project is to develop an easy to use, low cost CV technology stack for SMEs that can be used to create advanced applications, without any technical training and can seamlessly integrate into existing works flows to unlock value in already collected data.

**为中小企业释放计算机视觉的潜力-开发一个人工智能平台，使非技术用户能够构建先进的简历应用程序**视觉图像中有大量有用的信息和价值，但是计算机视觉专业知识的高成本以及分析和整理数据所需的时间，意味着目前英国中小企业不可能充分发挥其潜力。从帮助医生诊断医疗状况，到用无人机检测作物病害，再到确保建筑工地的安全协议，计算机视觉有可能在各个领域开启新的增长和效率机会。不幸的是，中小企业很少拥有所需的简历专业知识，而雇佣/签约这些专业知识的成本可能非常昂贵。即便如此，高技能的简历科学家也要花80美元% of their time on data curation alone\[3\], with their lack of domain expertise then leading to underperforming, unusable models\[4,5\]. Such challenges result in 55% of projects never making it to production\[3\].To increase adoption, solutions need to speed-up data curation processes, reduce curation costs and become more accessible to those without technical training. Organisations such as hospitals and manufacturers own both vast amounts of visual data and the domain expertise to interpret it, making them ideally placed to create impactful CV applications. Empowering them to build their own application can help accelerate AI-adoption and the resulting benefits.Collaborating with medical imaging experts at King's College London and retinal imaging SME Optos, Metalynx aims to significantly increase CV adoption amongst UK SMEs by removing the key barriers to adoption -- cost and expertise -- through the development of accessible tools to build advanced CV applications, easily.The ultimate aim of the project is to develop an easy to use, low cost CV technology stack for SMEs that can be used to create advanced applications, without any technical training and can seamlessly integrate into existing works flows to unlock value in already collected data.