Simultaneous mass estimation and class classification of scrap metals using deep learning

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Autores: Diaz-Romero, Dillam, Van den Eynde, Simon, Sterkens, Wouter, Engelen, Bart, Zaplana Agut, Isiah|||0000-0002-0862-3240, Dewulf, Wim, Goedemé, Toon, Peeters, Jef R.
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/381993
Acceso en línea:https://hdl.handle.net/2117/381993
https://dx.doi.org/10.1016/j.resconrec.2022.106272
Access Level:acceso abierto
Palabra clave:Scrap metals
Artificial intelligence
Automatic sorting
Metal recycling
Stainless steel
Cast and wrought aluminium scrap
Deep learning computer vision
Backpropagation neural network
Mass/weight prediction
Object detection and recognition
Residus metàl·lics
Intel·ligència artificial
Àrees temàtiques de la UPC::Desenvolupament humà i sostenible::Enginyeria ambiental::Tractament dels residus
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spelling Simultaneous mass estimation and class classification of scrap metals using deep learningDiaz-Romero, DillamVan den Eynde, SimonSterkens, WouterEngelen, BartZaplana Agut, Isiah|||0000-0002-0862-3240Dewulf, WimGoedemé, ToonPeeters, Jef R.Scrap metalsArtificial intelligenceArtificial intelligenceAutomatic sortingMetal recyclingStainless steelCast and wrought aluminium scrapDeep learning computer visionBackpropagation neural networkMass/weight predictionObject detection and recognitionResidus metàl·licsIntel·ligència artificialÀrees temàtiques de la UPC::Desenvolupament humà i sostenible::Enginyeria ambiental::Tractament dels residus© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksWhile deep learning has helped improve the performance of classification, object detection, and segmentation in recycling, its potential for mass prediction has not yet been explored. Therefore, this study proposes a system for mass prediction with and without feature extraction and selection, including principal component analysis (PCA). These feature extraction methods are evaluated on a combined Cast (C), Wrought (W) and Stainless Steel (SS) image dataset using state-of-the-art machine learning and deep learning algorithms for mass prediction. After that, the best mass prediction framework is combined with a DenseNet classifier, resulting in multiple outputs that perform both object classification and object mass prediction. The proposed architecture consists of a DenseNet neural network for classification and a backpropagation neural network (BPNN) for mass prediction, which uses up to 24 features extracted from depth images. The proposed method obtained 0.82 R2, 0.2 RMSE, and 0.28 MAE for the regression for mass prediction with a classification performance of 95% for the C&W test dataset using the DenseNet+BPNN+PCA model. The DenseNet+BPNN+None model without the selected feature (None) used for the CW&SS test data had a lower performance for both classification of 80% and the regression (0.71 R2, 0.31 RMSE, and 0.32 MAE). The presented method has the potential to improve the monitoring of the mass composition of waste streams and to optimize robotic and pneumatic sorting systems by providing a better understanding of the physical properties of the objects being sorted.Peer Reviewed20222022-06-0120232023-02-02journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/381993https://dx.doi.org/10.1016/j.resconrec.2022.106272reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3819932026-05-27T15:37:01Z
dc.title.none.fl_str_mv Simultaneous mass estimation and class classification of scrap metals using deep learning
title Simultaneous mass estimation and class classification of scrap metals using deep learning
spellingShingle Simultaneous mass estimation and class classification of scrap metals using deep learning
Diaz-Romero, Dillam
Scrap metals
Artificial intelligence
Artificial intelligence
Automatic sorting
Metal recycling
Stainless steel
Cast and wrought aluminium scrap
Deep learning computer vision
Backpropagation neural network
Mass/weight prediction
Object detection and recognition
Residus metàl·lics
Intel·ligència artificial
Àrees temàtiques de la UPC::Desenvolupament humà i sostenible::Enginyeria ambiental::Tractament dels residus
title_short Simultaneous mass estimation and class classification of scrap metals using deep learning
title_full Simultaneous mass estimation and class classification of scrap metals using deep learning
title_fullStr Simultaneous mass estimation and class classification of scrap metals using deep learning
title_full_unstemmed Simultaneous mass estimation and class classification of scrap metals using deep learning
title_sort Simultaneous mass estimation and class classification of scrap metals using deep learning
dc.creator.none.fl_str_mv Diaz-Romero, Dillam
Van den Eynde, Simon
Sterkens, Wouter
Engelen, Bart
Zaplana Agut, Isiah|||0000-0002-0862-3240
Dewulf, Wim
Goedemé, Toon
Peeters, Jef R.
author Diaz-Romero, Dillam
author_facet Diaz-Romero, Dillam
Van den Eynde, Simon
Sterkens, Wouter
Engelen, Bart
Zaplana Agut, Isiah|||0000-0002-0862-3240
Dewulf, Wim
Goedemé, Toon
Peeters, Jef R.
author_role author
author2 Van den Eynde, Simon
Sterkens, Wouter
Engelen, Bart
Zaplana Agut, Isiah|||0000-0002-0862-3240
Dewulf, Wim
Goedemé, Toon
Peeters, Jef R.
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Scrap metals
Artificial intelligence
Artificial intelligence
Automatic sorting
Metal recycling
Stainless steel
Cast and wrought aluminium scrap
Deep learning computer vision
Backpropagation neural network
Mass/weight prediction
Object detection and recognition
Residus metàl·lics
Intel·ligència artificial
Àrees temàtiques de la UPC::Desenvolupament humà i sostenible::Enginyeria ambiental::Tractament dels residus
topic Scrap metals
Artificial intelligence
Artificial intelligence
Automatic sorting
Metal recycling
Stainless steel
Cast and wrought aluminium scrap
Deep learning computer vision
Backpropagation neural network
Mass/weight prediction
Object detection and recognition
Residus metàl·lics
Intel·ligència artificial
Àrees temàtiques de la UPC::Desenvolupament humà i sostenible::Enginyeria ambiental::Tractament dels residus
description © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-06-01
2023
2023-02-02
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/381993
https://dx.doi.org/10.1016/j.resconrec.2022.106272
url https://hdl.handle.net/2117/381993
https://dx.doi.org/10.1016/j.resconrec.2022.106272
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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