气候变化领域集成服务门户(CCPortal): Machine learning for aquatic plastic litter detection, classification and quantification (APLASTIC-Q)

CCPortal

DOI	10.1088/1748-9326/abbd01
	Machine learning for aquatic plastic litter detection, classification and quantification (APLASTIC-Q)
	Wolf M.; Van Den Berg K.; Garaba S.P.; Gnann N.; Sattler K.; Stahl F.; Zielinski O.
发表日期	2020
ISSN	17489318
卷号	15 期号:11
英文摘要	Large quantities of mismanaged plastic waste are polluting and threatening the health of the blue planet. As such, vast amounts of this plastic waste found in the oceans originates from land. It finds its way to the open ocean through rivers, waterways and estuarine systems. Here we present a novel machine learning algorithm based on convolutional neural networks (CNNs) that is capable of detecting and quantifying floating and washed ashore plastic litter. The aquatic plastic litter detection, classification and quantification system (APLASTIC-Q) was developed and trained using very high geo-spatial resolution imagery (∼5 pixels cm-1 = 0.002 m pixel-1) captured from aerial surveys in Cambodia. APLASTIC-Q was made up of two machine learning components (i) plastic litter detector (PLD-CNN) and (ii) plastic litter quantifier (PLQ-CNN). PLD-CNN managed to categorize targets as water, sand, vegetation and plastic litter with an 83% accuracy. It also provided a qualitative count of litter as low or high based on a thresholding approach. PLQ-CNN further distinguished and enumerated the litter items in each of the classes defined as water bottles, Styrofoam, canisters, cartons, bowls, shoes, polystyrene packaging, cups, textile, carry bags small or large. The types and amounts of plastic litter provide benchmark information that is urgently needed for decision-making by policymakers, citizens and other public and private stakeholders. Quasi-quantification was based on automated counts of items present in the imagery with caveats of underlying object in case of aggregated litter. Our scientific evidence-based machine learning algorithm has the prospects of complementing net trawl surveys, field campaigns and clean-up activities for improved quantification of plastic litter. APLASTIC-Q is a smart algorithm that is easy to adapt for fast and automated detection as well as quantification of floating or washed ashore plastic litter from aerial, high-altitude pseudo satellites and space missions. © 2020 The Author(s). Published by IOP Publishing Ltd.
英文关键词	Cambodia; convolutional neural networks; detection; machine learning; plastic litter; remote sensing; river and beach ecosystems
语种	英语
scopus关键词	Antennas; Bottles; Convolutional neural networks; Decision making; Fisheries; Machine learning; Pixels; Surveys; Automated detection; Clean-up activities; Estuarine systems; Field campaign; Private stakeholders; Pseudo satellites; Scientific evidence; Smart algorithms; Learning algorithms; Satellites
来源期刊	Environmental Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/153477
作者单位	Marine Perception Research Group, German Research Centre for Artificial Intelligence (DFKI), Marie-Curie-Str. 1, Oldenburg, 26129, Germany; Marine Sensor Systems Group, Institute for Chemistry and Biology of the Marine Environment, Carl von Ossietzky University of Oldenburg, Schleusenstraße 1, Wilhelmshaven, 26382, Germany; Environment, Natural Resources and Blue Economy, The World Bank, 1818 H Street, NW, Washington, DC 20433, United States; Department of Computer Science, University of Reading, Reading, United Kingdom
推荐引用方式 GB/T 7714	Wolf M.,Van Den Berg K.,Garaba S.P.,et al. Machine learning for aquatic plastic litter detection, classification and quantification (APLASTIC-Q)[J],2020,15(11).
APA	Wolf M..,Van Den Berg K..,Garaba S.P..,Gnann N..,Sattler K..,...&Zielinski O..(2020).Machine learning for aquatic plastic litter detection, classification and quantification (APLASTIC-Q).Environmental Research Letters,15(11).
MLA	Wolf M.,et al."Machine learning for aquatic plastic litter detection, classification and quantification (APLASTIC-Q)".Environmental Research Letters 15.11(2020).