Deep learning for detecting macroplastic litter in water bodies: A review.

Jia, Tianlong; Kapelan, Zoran; de Vries, Rinze; Vriend, Paul; Peereboom, Eric Copius; Okkerman, Imke; Taormina, Riccardo

Jia, Tianlong; Kapelan, Zoran; de Vries, Rinze; Vriend, Paul; Peereboom, Eric Copius; Okkerman, Imke; Taormina, Riccardo.

Afiliação

Jia T; Delft University of Technology, Faculty of Civil Engineering and Geosciences, Department of Water Management, Stevinweg 1, 2628 CN Delft, The Netherlands. Electronic address: T.Jia@tudelft.nl.
Kapelan Z; Delft University of Technology, Faculty of Civil Engineering and Geosciences, Department of Water Management, Stevinweg 1, 2628 CN Delft, The Netherlands.
de Vries R; Noria Sustainable Innovators, Schieweg 13, 2627 AN Delft, The Netherlands.
Vriend P; Rijkswaterstaat, Ministry of Infrastructure and Water Management, Griffioenlaan 2, 3526 LA Utrecht, The Netherlands.
Peereboom EC; Rijkswaterstaat, Ministry of Infrastructure and Water Management, Griffioenlaan 2, 3526 LA Utrecht, The Netherlands.
Okkerman I; Rijkswaterstaat, Ministry of Infrastructure and Water Management, Griffioenlaan 2, 3526 LA Utrecht, The Netherlands.
Taormina R; Delft University of Technology, Faculty of Civil Engineering and Geosciences, Department of Water Management, Stevinweg 1, 2628 CN Delft, The Netherlands.

Water Res ; 231: 119632, 2023 Mar 01.

Article em En | MEDLINE | ID: mdl-36689878

Assuntos

Aprendizado Profundo; Ecossistema; Inteligência Artificial; Plásticos; Água; Monitoramento Ambiental; Resíduos/análise

Palavras-chave

Artificial intelligence; Computer vision; Environmental monitoring; Macroplastics; Neural networks; Pollution

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Ecossistema / Aprendizado Profundo Tipo de estudo: Prognostic_studies / Systematic_reviews Idioma: En Ano de publicação: 2023 Tipo de documento: Article

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