SegX-Net: A novel image segmentation approach for contrail detection using deep learning

Contrails are line-shaped clouds formed in the exhaust of aircraft engines that significantly contribute to global warming. This paper confidently proposes integrating advanced image segmentation techniques to identify and monitor aircraft contrails to address the challenges associated with climate...

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Detalles Bibliográficos
Autores: Nobel, S. M.Nuruzzaman, Hossain, Md Ashraful, Kabir, Md Mohsin, Mridha, M. F., Alfarhood, Sultan, Safran, Mejdl
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10256/25532
Acceso en línea:http://hdl.handle.net/10256/25532
Access Level:acceso abierto
Palabra clave:Imatges -- Segmentació
Imaging segmentation
Escalfament global
Global warming
Canvis climàtics -- Mitigació
Climate change mitigation
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spelling SegX-Net: A novel image segmentation approach for contrail detection using deep learningNobel, S. M.NuruzzamanHossain, Md AshrafulKabir, Md MohsinMridha, M. F.Alfarhood, SultanSafran, MejdlImatges -- SegmentacióImaging segmentationEscalfament globalGlobal warmingCanvis climàtics -- MitigacióClimate change mitigationContrails are line-shaped clouds formed in the exhaust of aircraft engines that significantly contribute to global warming. This paper confidently proposes integrating advanced image segmentation techniques to identify and monitor aircraft contrails to address the challenges associated with climate change. We propose the SegX-Net architecture, a highly efficient and lightweight model that combines the DeepLabV3+, upgraded, and ResNet-101 architectures to achieve superior segmentation accuracy. We evaluated the performance of our model on a comprehensive dataset from Google research and rigorously measured its efficacy with metrics such as IoU, F1 score, Sensitivity and Dice Coefficient. Our results demonstrate that our enhancements have significantly improved the efficacy of the SegX-Net model, with an outstanding IoU score of 98.86% and an impressive F1 score of 99.47%. These results unequivocally demonstrate the potential of image segmentation methods to effectively address and mitigate the impact of air conflict on global warming. Using our proposed SegX-Net architecture, stakeholders in the aviation industry can confidently monitor and mitigate the impact of aircraft shrinkage on the environment, significantly contributing to the global fight against climate change13Public Library of Science (PLoS)2024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionpeer-reviewedapplication/pdfhttp://hdl.handle.net/10256/25532http://hdl.handle.net/10256/25532PLoS ONE, 2024, vol. 19, núm. 3, p. e0298160Articles publicats (D-ATC)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)Inglésinfo:eu-repo/semantics/altIdentifier/doi/10.1371/journal.pone.0298160info:eu-repo/semantics/altIdentifier/eissn/1932-6203Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10256/255322026-05-29T05:05:01Z
dc.title.none.fl_str_mv SegX-Net: A novel image segmentation approach for contrail detection using deep learning
title SegX-Net: A novel image segmentation approach for contrail detection using deep learning
spellingShingle SegX-Net: A novel image segmentation approach for contrail detection using deep learning
Nobel, S. M.Nuruzzaman
Imatges -- Segmentació
Imaging segmentation
Escalfament global
Global warming
Canvis climàtics -- Mitigació
Climate change mitigation
title_short SegX-Net: A novel image segmentation approach for contrail detection using deep learning
title_full SegX-Net: A novel image segmentation approach for contrail detection using deep learning
title_fullStr SegX-Net: A novel image segmentation approach for contrail detection using deep learning
title_full_unstemmed SegX-Net: A novel image segmentation approach for contrail detection using deep learning
title_sort SegX-Net: A novel image segmentation approach for contrail detection using deep learning
dc.creator.none.fl_str_mv Nobel, S. M.Nuruzzaman
Hossain, Md Ashraful
Kabir, Md Mohsin
Mridha, M. F.
Alfarhood, Sultan
Safran, Mejdl
author Nobel, S. M.Nuruzzaman
author_facet Nobel, S. M.Nuruzzaman
Hossain, Md Ashraful
Kabir, Md Mohsin
Mridha, M. F.
Alfarhood, Sultan
Safran, Mejdl
author_role author
author2 Hossain, Md Ashraful
Kabir, Md Mohsin
Mridha, M. F.
Alfarhood, Sultan
Safran, Mejdl
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Imatges -- Segmentació
Imaging segmentation
Escalfament global
Global warming
Canvis climàtics -- Mitigació
Climate change mitigation
topic Imatges -- Segmentació
Imaging segmentation
Escalfament global
Global warming
Canvis climàtics -- Mitigació
Climate change mitigation
description Contrails are line-shaped clouds formed in the exhaust of aircraft engines that significantly contribute to global warming. This paper confidently proposes integrating advanced image segmentation techniques to identify and monitor aircraft contrails to address the challenges associated with climate change. We propose the SegX-Net architecture, a highly efficient and lightweight model that combines the DeepLabV3+, upgraded, and ResNet-101 architectures to achieve superior segmentation accuracy. We evaluated the performance of our model on a comprehensive dataset from Google research and rigorously measured its efficacy with metrics such as IoU, F1 score, Sensitivity and Dice Coefficient. Our results demonstrate that our enhancements have significantly improved the efficacy of the SegX-Net model, with an outstanding IoU score of 98.86% and an impressive F1 score of 99.47%. These results unequivocally demonstrate the potential of image segmentation methods to effectively address and mitigate the impact of air conflict on global warming. Using our proposed SegX-Net architecture, stakeholders in the aviation industry can confidently monitor and mitigate the impact of aircraft shrinkage on the environment, significantly contributing to the global fight against climate change
publishDate 2024
dc.date.none.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
peer-reviewed
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10256/25532
http://hdl.handle.net/10256/25532
url http://hdl.handle.net/10256/25532
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1371/journal.pone.0298160
info:eu-repo/semantics/altIdentifier/eissn/1932-6203
dc.rights.none.fl_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Public Library of Science (PLoS)
publisher.none.fl_str_mv Public Library of Science (PLoS)
dc.source.none.fl_str_mv PLoS ONE, 2024, vol. 19, núm. 3, p. e0298160
Articles publicats (D-ATC)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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