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Title: Deep learning approach for automatic microplastics counting and classification
Authors: Lorenzo Navarro, Javier 
Castrillón-Santana, Modesto 
Sánchez-Nielsen, Elena
Zarco, Borja
Herrera, Alicia 
Martínez, Ico 
Gómez, May 
UNESCO Clasification: 120317 Informática
330811 Control de la contaminación del agua
Keywords: Microplastics
Image analysis
Deep Learning
Classification Image analysis
Artificial intelligence
Issue Date: 2021
Project: Estudio de la incorporación de microplásticos marinos a las redes tróficas en Canarias 
Evaluación del impacto de microplásticos y contaminantes emergentes en las costas de la Macaronesia 
Evaluación de Los Riesgos Derivados de la Contaminación Marina Por Microplásticos 
TIN2016-78919-R (Ministerio de Ciencia e Innovación)
PID2019-107228RB-I00 (Ministerio de Ciencia e Innovación)
Journal: Science of the Total Environment 
Abstract: The quantification of microplastics is a needed task to monitor its evolution and model its behavior.However, it isa time demanding task traditionally performed using expensive equipment. In this paper, an architecture basedon deep learning networks is presented with the aim of automatically count and classify microplastic particles inthe range of 1–5 mm from pictures taken with a digital camera or a mobile phone with a resolution of 16 millionpixels or higher. The proposed architecture comprises afirst stage, implemented with the U-Net neural network,in charge of making the segmentation of the particles in the image. After the different particles have been iso-lated, a second stage based on the VGG16 neural network classifies them into three types: fragments, pelletsand lines. These threetypeshave been selected for beingthe mostcommon in the range sizeunder consideration.The experimentalevaluation was carried out usingimages taken with two digitalcameras and one mobilephone.The particles used in experiments correspond to samples collected on the beach of Playa del Poris in Tenerife Is-land, Spain, (28° 09′51′′N, 16° 25′54′′W) in August 2018. A Jaccard index value of 0.8 is achieved in the exper-iments of particles segmentation and an accuracy of 98.11% is obtained in the classification of the microplasticparticles. The proposedarchitecture is remarkablefaster than a similar previously published system based on tra-ditional computer vision techniques.
ISSN: 0048-9697
DOI: 10.1016/j.scitotenv.2020.142728
Source: Science of the Total Environment [ISSN 0048-9697], v. 765, 142728 (Abril 2021)
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