The use of artificial intelligence algorithms to detect macroplastics in aquatic environments: A critical review.

Autor: Astorayme MA; Peruvian Life Cycle Assessment & Industrial Ecology Network (PELCAN), Department of Engineering, Pontificia Universidad Católica del Perú, Av. Universitaria 1801, San Miguel 15074, Lima, Peru; Dept. of Fluid Mechanics Engineering, Universidad Nacional Mayor de San Marcos, Av. Universitaria/Av. Germán Amézaga s/n., Lima 1508, Lima, Peru.. Electronic address: m.astoraymev@pucp.edu.pe., Vázquez-Rowe I; Peruvian Life Cycle Assessment & Industrial Ecology Network (PELCAN), Department of Engineering, Pontificia Universidad Católica del Perú, Av. Universitaria 1801, San Miguel 15074, Lima, Peru., Kahhat R; Peruvian Life Cycle Assessment & Industrial Ecology Network (PELCAN), Department of Engineering, Pontificia Universidad Católica del Perú, Av. Universitaria 1801, San Miguel 15074, Lima, Peru.
Jazyk: angličtina
Zdroj: The Science of the total environment [Sci Total Environ] 2024 Oct 01; Vol. 945, pp. 173843. Date of Electronic Publication: 2024 Jun 11.
DOI: 10.1016/j.scitotenv.2024.173843
Abstrakt: The presence of macroplastic (MP) is having serious consequences on natural ecosystems, directly affecting biota and human wellbeing. Given this scenario, estimating MPs' abundance is crucial for assessing the issue and formulating effective waste management strategies. In this context, the main objective of this critical review is to analyze the use of machine learning (ML) techniques, with a particular interest in deep learning (DL) approaches, to detect, classify and quantify MPs in aquatic environments, supported by datasets such as satellite or aerial images and video recordings taken by unmanned aerial vehicles. This article provides a concise overview of artificial intelligence concepts, followed by a bibliometric analysis and a critical review. The search methodology aimed to categorize the scientific contributions through temporal and spatial criteria for bibliometric analysis, whereas the critical review was based on generating homogeneous groups according to the complexity of ML and DL methods, as well as the type of dataset. In light of the review carried out, classical ML techniques, such as random forest or support vector machines, showed robustness in MPs detection. However, it seems that achieving optimal efficiencies in multiclass classification is a limitation for these methods. Consequently, more advanced techniques such as DL approaches are taking the lead for the detection and multiclass classification of MPs. A series of architectures based on convolutional neural networks, and the use of complex pre-trained models through the transfer learning, are currently being explored (e.g., VGG16 and YOLO models), although currently the computational expense is high due to the need for processing large volumes of data. Additionally, there seems to be a trend towards detecting smaller plastic, which need higher resolution images. Finally, it is important to stress that since 2020 there has been a significant increase in scientific research focusing on transformer-based architectures for object detection. Although this can be considered the current state of the art, no studies have been identified that utilize these architectures for MP detection.
Competing Interests: Declaration of competing interest The authors declare no conflict of interest.
(Copyright © 2024 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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