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CopperGuard - AI-enabled metal theft prevention

CopperGuard - 支持人工智能的金属防盗

基本信息

批准号：
105032
负责人：
金额：
$ 43.66万
依托单位：
ARCHANGEL IMAGING LTD.
依托单位国家：
英国
项目类别：
Feasibility Studies
财政年份：
2019
资助国家：
英国
起止时间：
2019 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=105032
关键词：
CopperGuard AI enabled metal theft

项目摘要

"Archangel Imaging's project will employ AI-enabled camera systems to help stem the cost to business and the taxpayer of widespread metal theft. Around the globe, metals ranging from copper to steel (telecom, rail and power cables and infrastructure) are being stolen at an increasing rate; in the UK alone, it is estimated to be heading towards a £1Bn p.a. area of criminal activity.In 2017 the price of copper leapt by 50%. The effect of this translates into a spike in copper theft (a global problem).Taken on a global scale, metal theft is a significant and growing problem and normally it is the taxpayer who covers the cost. Safety is often compromised when criminals degrade rail network or power systems, even when no-one is hurt, the theft usually translates as inconvenience to the public due to communications failure, power outages or rail delays as repairs can often be time consuming and costly. The global market for systems to address the growing multi-billion p.a. metal theft situation is in the hundreds of millions of pounds. The proposed project will develop innovative, low cost technology to allow companies to better protect their valuable infrastructure; the technology easily translates into other areas such as the oil and gas sector, airport security, road safety, in fact wherever routine surveillance is undertaken."

“大天使成像公司的项目将使用人工智能相机系统，以帮助遏制广泛的金属盗窃给企业和纳税人带来的成本。在全球范围内，从铜到钢的各种金属(电信、铁路、电力电缆和基础设施)正以越来越快的速度被盗；仅在英国，估计每年就有10亿GB的金属被盗。犯罪领域。2017年，铜价跃升了50%。其影响转化为铜盗窃的激增(一个全球问题)。从全球范围来看，金属盗窃是一个严重且日益严重的问题，通常是纳税人来承担成本。当犯罪分子破坏铁路网或电力系统时，安全往往会受到影响，即使没有人受伤，这种盗窃通常也会转化为由于通信故障、停电或铁路延误而给公众带来的不便，因为维修往往既耗时又昂贵。全球系统市场，以应对每年数十亿美元的增长。金属盗窃案的规模高达数亿英镑。拟议中的项目将开发创新的低成本技术，使公司能够更好地保护其宝贵的基础设施；这项技术很容易转化为其他领域，如石油和天然气部门、机场安全、道路安全，实际上是在任何进行例行监控的领域。