Machine learning for aquatic plastic litter detection, classification and quantification (APLASTIC-Q)

Mattis Wolf,Oliver Zielinski,Frederic Stahl,Nina Gnann,Klaus Sattler,Shungudzemwoyo P Garaba,Katelijn Van Den Berg

doi:10.1088/1748-9326/abbd01

Abstract

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.

Highlights

Plastic pollution is a ‘wicked environmental problem’ with annual estimates indicating global rivers discharging several million metric tonnes of plastic waste into the oceans (Balint et al 2011, Jambeck et al 2015, Lebreton et al 2017)
Litter class distribution in datasets PLD-convolutional neural networks (CNNs) dataset was composed of 6892 tiles, a total of 1905 tiles contained high amounts of plastic litter were grouped as litter-high
We presented a novel machine learning system that performed reasonably well in identifying and quantifying floating and washed ashore plastic litter in terms of covered areas and counts of litter items

Summary

Introduction

Plastic pollution is a ‘wicked environmental problem’ with annual estimates indicating global rivers discharging several million metric tonnes of plastic waste into the oceans (Balint et al 2011, Jambeck et al 2015, Lebreton et al 2017). Transport substantial amounts of plastics into the open ocean (Sethy et al 2014, Lebreton et al 2017, van Emmerik et al 2019) These plastic polluted waterways do pose localized health and environmental problems but a global threat to the blue economy (Todd et al 2010, Blettler et al 2018). As a result of these events, it is suspected that plastic leakage is enhanced into rivers, and subsequently shallow sea and the ocean After these periods of flooding or even high tides, plastic litter is washed ashore or trapped by vegetation, whereas the remainder is transported offshore. This is in line with large scale political initiatives like the EU Marine Strategy Framework Directive’s descriptor 10 (Galgani et al 2013), the Single-use Plastics Directive 2019 (EUPPD 2019), UN Sustainable Development Goal 14 target 14.1 to ‘prevent and significantly reduce marine pollutions of all kinds’ (Recuero Virto 2018), even the UN ‘Decade of Ocean Science for Sustainable Development (2021–2030)’ aiming to ‘support efforts to reverse the cycle of decline in ocean health and create improved conditions for sustainable development of the ocean’ (Ryabinin et al 2019, IOC-UNESCO 2019)

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Results

Conclusion