Material measurement units for a circular economy: Foundations through a review

被引:5
作者
Zocco, Federico [1 ]
McLoone, Sean [1 ]
Smyth, Beatrice [2 ]
机构
[1] Queens Univ Belfast, Ctr Intelligent Autonomous Mfg Syst, Sch Elect Elect Engn & Comp Sci, Belfast, North Ireland
[2] Queens Univ Belfast, Res Ctr Sustainable Energy, Sch Mech & Aerosp Engn, Belfast, North Ireland
关键词
Computer vision; Machine learning; Deep learning; Industrial ecology; Circular economy; SOLID-WASTE BIN; FOOD RECOGNITION; RECOVERY; CLASSIFICATION; FEATURES; TEXTURE; METALS; SYSTEM; BOARDS; TECHNOLOGIES;
D O I
10.1016/j.spc.2022.05.022
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Long-term availability of minerals and industrial materials is a necessary condition for sustainable development as they are the constituents of any manufacturing product. To enhance the efficiency of material management, we define a computer-vision-enabled material measurement system and provide a review of works relevant to its development with particular emphasis on the foundations. A network of such systems for wide-area material stock monitoring is also covered. Finally, challenges and future research directions are discussed. As the first article bridging industrial ecology and advanced computer vision, this review is intended to support both research communities towards more sustainable manufacturing. (c) 2022 The Authors. Published by Elsevier Ltd on behalf of Institution of Chemical Engineers. This is an open access Keywords: article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
引用
收藏
页码:833 / 850
页数:18
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