Classification of aluminum scrap by laser induced breakdown spectroscopy (LIBS) and RGB + D image fusion using deep learning approaches

Integrating multi-sensor systems to sort and monitor complex waste streams is one of the most recent innovations in the recycling industry. The complementary strengths of Laser-Induced Breakdown Spectroscopy (LIBS) and computer vision systems offer a novel multi-sensor solution for the complex task...

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Autores: Diaz-Romero, Dillam, Van den Eynde, Simon, Zaplana Agut, Isiah|||0000-0002-0862-3240, Sterkens, Wouter, Goedemé, Toon, Peeters, Jef R.
Formato: artículo
Fecha de publicación:2023
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/383642
Acesso em linha:https://hdl.handle.net/2117/383642
https://dx.doi.org/10.1016/j.resconrec.2023.106865
Access Level:acceso abierto
Palavra-chave:Aluminum -- Recycling
Scrap metals
Alumini -- Reciclatge
Residus metàl·lics
Àrees temàtiques de la UPC::Enginyeria dels materials
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spelling Classification of aluminum scrap by laser induced breakdown spectroscopy (LIBS) and RGB + D image fusion using deep learning approachesDiaz-Romero, DillamVan den Eynde, SimonZaplana Agut, Isiah|||0000-0002-0862-3240Sterkens, WouterGoedemé, ToonPeeters, Jef R.Aluminum -- RecyclingScrap metalsAlumini -- ReciclatgeResidus metàl·licsÀrees temàtiques de la UPC::Enginyeria dels materialsIntegrating multi-sensor systems to sort and monitor complex waste streams is one of the most recent innovations in the recycling industry. The complementary strengths of Laser-Induced Breakdown Spectroscopy (LIBS) and computer vision systems offer a novel multi-sensor solution for the complex task of sorting aluminum (Al) post-consumer scrap into alloy groups. This study presents two novel methods for fusing RGB and Depth images with LIBS using Deep Learning models. The first method is a single-output model that combines LIBS UNET and two DenseNets in a late fusion framework. The second method is a multiple-output model that uses the structure of the single-output model to enhance learning and avoid overfitting. In particular, the network has two outputs that enable the regularization of the individual sensors. A data set of 773 aluminum scrap pieces was created with two sets of ground truth-values, corresponding to the two envisaged sorting tasks, to train and evaluate the developed models. The first sorting task is separating Cast and Wrought (C&W) aluminum. The second is the division of the post-consumer aluminum scrap into three commercially interesting fractions. The single-output model performs best for separating C&W, with a Precision, Recall, and F1-score of 99%. The multiple-output model performs best for classifying the three selected commercial fractions, with a Precision, Recall, and F-score of 86%, 83%, and 84%, respectively. The presented data fusion method for LIBS and computer vision images encompasses the great potential for sorting post-consumer aluminum scrap. By sorting mixed post-consumer aluminum scrap in alloy groups, more wrought-to-wrought recycling can occur, and quality losses can be mitigated during recycling.Peer Reviewed20232023-03-0120232023-02-17journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/383642https://dx.doi.org/10.1016/j.resconrec.2023.106865reponame: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:2117/3836422026-05-27T15:37:01Z
dc.title.none.fl_str_mv Classification of aluminum scrap by laser induced breakdown spectroscopy (LIBS) and RGB + D image fusion using deep learning approaches
title Classification of aluminum scrap by laser induced breakdown spectroscopy (LIBS) and RGB + D image fusion using deep learning approaches
spellingShingle Classification of aluminum scrap by laser induced breakdown spectroscopy (LIBS) and RGB + D image fusion using deep learning approaches
Diaz-Romero, Dillam
Aluminum -- Recycling
Scrap metals
Alumini -- Reciclatge
Residus metàl·lics
Àrees temàtiques de la UPC::Enginyeria dels materials
title_short Classification of aluminum scrap by laser induced breakdown spectroscopy (LIBS) and RGB + D image fusion using deep learning approaches
title_full Classification of aluminum scrap by laser induced breakdown spectroscopy (LIBS) and RGB + D image fusion using deep learning approaches
title_fullStr Classification of aluminum scrap by laser induced breakdown spectroscopy (LIBS) and RGB + D image fusion using deep learning approaches
title_full_unstemmed Classification of aluminum scrap by laser induced breakdown spectroscopy (LIBS) and RGB + D image fusion using deep learning approaches
title_sort Classification of aluminum scrap by laser induced breakdown spectroscopy (LIBS) and RGB + D image fusion using deep learning approaches
dc.creator.none.fl_str_mv Diaz-Romero, Dillam
Van den Eynde, Simon
Zaplana Agut, Isiah|||0000-0002-0862-3240
Sterkens, Wouter
Goedemé, Toon
Peeters, Jef R.
author Diaz-Romero, Dillam
author_facet Diaz-Romero, Dillam
Van den Eynde, Simon
Zaplana Agut, Isiah|||0000-0002-0862-3240
Sterkens, Wouter
Goedemé, Toon
Peeters, Jef R.
author_role author
author2 Van den Eynde, Simon
Zaplana Agut, Isiah|||0000-0002-0862-3240
Sterkens, Wouter
Goedemé, Toon
Peeters, Jef R.
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Aluminum -- Recycling
Scrap metals
Alumini -- Reciclatge
Residus metàl·lics
Àrees temàtiques de la UPC::Enginyeria dels materials
topic Aluminum -- Recycling
Scrap metals
Alumini -- Reciclatge
Residus metàl·lics
Àrees temàtiques de la UPC::Enginyeria dels materials
description Integrating multi-sensor systems to sort and monitor complex waste streams is one of the most recent innovations in the recycling industry. The complementary strengths of Laser-Induced Breakdown Spectroscopy (LIBS) and computer vision systems offer a novel multi-sensor solution for the complex task of sorting aluminum (Al) post-consumer scrap into alloy groups. This study presents two novel methods for fusing RGB and Depth images with LIBS using Deep Learning models. The first method is a single-output model that combines LIBS UNET and two DenseNets in a late fusion framework. The second method is a multiple-output model that uses the structure of the single-output model to enhance learning and avoid overfitting. In particular, the network has two outputs that enable the regularization of the individual sensors. A data set of 773 aluminum scrap pieces was created with two sets of ground truth-values, corresponding to the two envisaged sorting tasks, to train and evaluate the developed models. The first sorting task is separating Cast and Wrought (C&W) aluminum. The second is the division of the post-consumer aluminum scrap into three commercially interesting fractions. The single-output model performs best for separating C&W, with a Precision, Recall, and F1-score of 99%. The multiple-output model performs best for classifying the three selected commercial fractions, with a Precision, Recall, and F-score of 86%, 83%, and 84%, respectively. The presented data fusion method for LIBS and computer vision images encompasses the great potential for sorting post-consumer aluminum scrap. By sorting mixed post-consumer aluminum scrap in alloy groups, more wrought-to-wrought recycling can occur, and quality losses can be mitigated during recycling.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-03-01
2023
2023-02-17
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/2117/383642
https://dx.doi.org/10.1016/j.resconrec.2023.106865
url https://hdl.handle.net/2117/383642
https://dx.doi.org/10.1016/j.resconrec.2023.106865
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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