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

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.

Marine Citizen Science: Current State in Europe and New Technological Developments

“…An example of AI-supported analysis of sensor data is the World Bank-funded initiative to collect plastic waste information over Asian rivers (Wolf et al, 2020). Research shows that more than 2/3 of the plastic waste in the ocean is discharged by just 20 rivers, most of it in Asia (Lebreton et al, 2017;Schmidt et al, 2017).…”

“…Research shows that more than 2/3 of the plastic waste in the ocean is discharged by just 20 rivers, most of it in Asia (Lebreton et al, 2017;Schmidt et al, 2017). Wolf et al (2020) use multispectral image data of drone flights from Cambodia, the Philippines and Myanmar to determine both the amount and the composition of the debris using a two-step approach of artificial neural networks. The former is relevant for efficient waste disposal, while the detailed information on individual waste components (cups, food packaging, and transport containers) helps local authorities to identify the sources of plastic waste and to take countermeasures.…”

Towards Detecting Floating Objects on a Global Scale With Learned Spatial Features Using Sentinel 2

“…Nonetheless, visual inspections and the use of statistical data analysis techniques are still used (Lebreton et al, 2018). In terms of methods, Wolf et al (2020) also used a Convolutional Neural Network (CNN) but focused on high-resolution UAV images for the detection and quantification of plastic litter. While UAV imagery provides imagery of high-quality that is well-suited for a machine learning approach, the availability of UAV imagery is inherently limited due to the acquisition costs.…”

“…At large-scale, this data is provided by sensors, such as Sentinel 2, with a moderate spatial resolution of 10 meters at which the detection of floating objects is challenging. High-resolution alternatives, such as UAV acquisitions, have been proposed in the literature (Wolf et al, 2020;Papakonstantinou et al, 2021) but scale poorly when monitoring hundreds of kilometers at frequent intervals. When floating objects agglomerate in the middle of the sea, it becomes challenging and even impossible to track them with drones or satellites.…”

Ai in Support to Water Quality Monitoring

“…More precisely, the presence of foams and algae is considered, as it is the most relevant and reported issue in the Insubric lakes. The availability of studies on the use of AI in order to identify phenomena linked to water quality is limited, and the majority of themto the best of our knowledgeis based on detecting litter on the water surface (Garcia-Garin et al, 2021;Wolf et al, 2020). AI is also used to detect harmful algal blooms (HAB) by analysing satellite images (Hill et al, 2020).…”