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Applying a deep residual network coupling with transfer learning for recyclable waste sorting

Recyclable waste sorting has become a key step for promoting the development of a circular economy with the gradual realization of carbon neutrality around the world. This study aims to develop an intelligent and efficient method for recyclable waste sorting by the method of deep learning. Thus, RWN...

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Autores principales: Lin, Kunsen, Zhao, Youcai, Gao, Xiaofeng, Zhang, Meilan, Zhao, Chunlong, Peng, Lu, Zhang, Qian, Zhou, Tao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9323877/
https://www.ncbi.nlm.nih.gov/pubmed/35882737
http://dx.doi.org/10.1007/s11356-022-22167-w
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author Lin, Kunsen
Zhao, Youcai
Gao, Xiaofeng
Zhang, Meilan
Zhao, Chunlong
Peng, Lu
Zhang, Qian
Zhou, Tao
author_facet Lin, Kunsen
Zhao, Youcai
Gao, Xiaofeng
Zhang, Meilan
Zhao, Chunlong
Peng, Lu
Zhang, Qian
Zhou, Tao
author_sort Lin, Kunsen
collection PubMed
description Recyclable waste sorting has become a key step for promoting the development of a circular economy with the gradual realization of carbon neutrality around the world. This study aims to develop an intelligent and efficient method for recyclable waste sorting by the method of deep learning. Thus, RWNet models, which refers to various ResNet structures (ResNet-18, ResNet-34, ResNet-50, ResNet-101, and ResNet-152) based on transfer learning, were proposed to classify different types of recyclable waste. Cyclical learning rate and data augmentation were taken to improve the performance of RWNet models. In addition, accuracy, precision, recall, F1 score, and ROC were taken to evaluate the performance of RWNet models. Results showed that the accuracy of various RWNet models is almost at 88%, and the best accuracy is 88.8% in RWNet-152. The highest precision, recall, and F1 score in terms of weighted average value appeared in RWNet-101 (89.9%), RWNet-152 (88.8%), and RWNet-152 (88.9%), respectively. The area under the ROC curve (AUC) is higher than 0.9, except for the AUC value of plastic (0.85), which indicated that most of the recyclable waste can be well sorted by RWNet models. This study demonstrates the good performance of RWNet models that can be used to automatically sort most of the recyclable waste, which paves the way for better recyclable waste management. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11356-022-22167-w.
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spelling pubmed-93238772022-07-27 Applying a deep residual network coupling with transfer learning for recyclable waste sorting Lin, Kunsen Zhao, Youcai Gao, Xiaofeng Zhang, Meilan Zhao, Chunlong Peng, Lu Zhang, Qian Zhou, Tao Environ Sci Pollut Res Int Research Article Recyclable waste sorting has become a key step for promoting the development of a circular economy with the gradual realization of carbon neutrality around the world. This study aims to develop an intelligent and efficient method for recyclable waste sorting by the method of deep learning. Thus, RWNet models, which refers to various ResNet structures (ResNet-18, ResNet-34, ResNet-50, ResNet-101, and ResNet-152) based on transfer learning, were proposed to classify different types of recyclable waste. Cyclical learning rate and data augmentation were taken to improve the performance of RWNet models. In addition, accuracy, precision, recall, F1 score, and ROC were taken to evaluate the performance of RWNet models. Results showed that the accuracy of various RWNet models is almost at 88%, and the best accuracy is 88.8% in RWNet-152. The highest precision, recall, and F1 score in terms of weighted average value appeared in RWNet-101 (89.9%), RWNet-152 (88.8%), and RWNet-152 (88.9%), respectively. The area under the ROC curve (AUC) is higher than 0.9, except for the AUC value of plastic (0.85), which indicated that most of the recyclable waste can be well sorted by RWNet models. This study demonstrates the good performance of RWNet models that can be used to automatically sort most of the recyclable waste, which paves the way for better recyclable waste management. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11356-022-22167-w. Springer Berlin Heidelberg 2022-07-26 2022 /pmc/articles/PMC9323877/ /pubmed/35882737 http://dx.doi.org/10.1007/s11356-022-22167-w Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Research Article
Lin, Kunsen
Zhao, Youcai
Gao, Xiaofeng
Zhang, Meilan
Zhao, Chunlong
Peng, Lu
Zhang, Qian
Zhou, Tao
Applying a deep residual network coupling with transfer learning for recyclable waste sorting
title Applying a deep residual network coupling with transfer learning for recyclable waste sorting
title_full Applying a deep residual network coupling with transfer learning for recyclable waste sorting
title_fullStr Applying a deep residual network coupling with transfer learning for recyclable waste sorting
title_full_unstemmed Applying a deep residual network coupling with transfer learning for recyclable waste sorting
title_short Applying a deep residual network coupling with transfer learning for recyclable waste sorting
title_sort applying a deep residual network coupling with transfer learning for recyclable waste sorting
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9323877/
https://www.ncbi.nlm.nih.gov/pubmed/35882737
http://dx.doi.org/10.1007/s11356-022-22167-w
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