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Research on Waste Plastics Classification Method Based on Multi-Scale Feature Fusion
Microplastic particles produced by non-degradable waste plastic bottles have a critical impact on the environment. Reasonable recycling is a premise that protects the environment and improves economic benefits. In this paper, a multi-scale feature fusion method for RGB and hyperspectral images based...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9609436/ https://www.ncbi.nlm.nih.gov/pubmed/36298325 http://dx.doi.org/10.3390/s22207974 |
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author | Cai, Zhenxing Yang, Jianhong Fang, Huaiying Ji, Tianchen Hu, Yangyang Wang, Xin |
author_facet | Cai, Zhenxing Yang, Jianhong Fang, Huaiying Ji, Tianchen Hu, Yangyang Wang, Xin |
author_sort | Cai, Zhenxing |
collection | PubMed |
description | Microplastic particles produced by non-degradable waste plastic bottles have a critical impact on the environment. Reasonable recycling is a premise that protects the environment and improves economic benefits. In this paper, a multi-scale feature fusion method for RGB and hyperspectral images based on Segmenting Objects by Locations (RHFF-SOLOv1) is proposed, which uses multi-sensor fusion technology to improve the accuracy of identifying transparent polyethylene terephthalate (PET) bottles, blue PET bottles, and transparent polypropylene (PP) bottles on a black conveyor belt. A line-scan camera and near-infrared (NIR) hyperspectral camera covering the spectral range from 935.9 nm to 1722.5 nm are used to obtain RGB and hyperspectral images synchronously. Moreover, we propose a hyperspectral feature band selection method that effectively reduces the dimensionality and selects the bands from 1087.6 nm to 1285.1 nm as the features of the hyperspectral image. The results show that the proposed fusion method improves the accuracy of plastic bottle classification compared with the SOLOv1 method, and the overall accuracy is 95.55%. Finally, compared with other space-spectral fusion methods, RHFF-SOLOv1 is superior to most of them and achieves the best (97.5%) accuracy in blue bottle classification. |
format | Online Article Text |
id | pubmed-9609436 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96094362022-10-28 Research on Waste Plastics Classification Method Based on Multi-Scale Feature Fusion Cai, Zhenxing Yang, Jianhong Fang, Huaiying Ji, Tianchen Hu, Yangyang Wang, Xin Sensors (Basel) Article Microplastic particles produced by non-degradable waste plastic bottles have a critical impact on the environment. Reasonable recycling is a premise that protects the environment and improves economic benefits. In this paper, a multi-scale feature fusion method for RGB and hyperspectral images based on Segmenting Objects by Locations (RHFF-SOLOv1) is proposed, which uses multi-sensor fusion technology to improve the accuracy of identifying transparent polyethylene terephthalate (PET) bottles, blue PET bottles, and transparent polypropylene (PP) bottles on a black conveyor belt. A line-scan camera and near-infrared (NIR) hyperspectral camera covering the spectral range from 935.9 nm to 1722.5 nm are used to obtain RGB and hyperspectral images synchronously. Moreover, we propose a hyperspectral feature band selection method that effectively reduces the dimensionality and selects the bands from 1087.6 nm to 1285.1 nm as the features of the hyperspectral image. The results show that the proposed fusion method improves the accuracy of plastic bottle classification compared with the SOLOv1 method, and the overall accuracy is 95.55%. Finally, compared with other space-spectral fusion methods, RHFF-SOLOv1 is superior to most of them and achieves the best (97.5%) accuracy in blue bottle classification. MDPI 2022-10-19 /pmc/articles/PMC9609436/ /pubmed/36298325 http://dx.doi.org/10.3390/s22207974 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Cai, Zhenxing Yang, Jianhong Fang, Huaiying Ji, Tianchen Hu, Yangyang Wang, Xin Research on Waste Plastics Classification Method Based on Multi-Scale Feature Fusion |
title | Research on Waste Plastics Classification Method Based on Multi-Scale Feature Fusion |
title_full | Research on Waste Plastics Classification Method Based on Multi-Scale Feature Fusion |
title_fullStr | Research on Waste Plastics Classification Method Based on Multi-Scale Feature Fusion |
title_full_unstemmed | Research on Waste Plastics Classification Method Based on Multi-Scale Feature Fusion |
title_short | Research on Waste Plastics Classification Method Based on Multi-Scale Feature Fusion |
title_sort | research on waste plastics classification method based on multi-scale feature fusion |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9609436/ https://www.ncbi.nlm.nih.gov/pubmed/36298325 http://dx.doi.org/10.3390/s22207974 |
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