A comparative study of convolutional neural network and transformer architectures for drone detection in thermal images

The widespread growth of drone technology is generating new security paradigms, especially with regard to the unauthorized activities of UAVs in restricted or sensitive areas, as well as illegal and illicit activities or attacks. Among the various UAV detection technologies, vision systems in differ...

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Autores: Gutiérrez, Gian, Llerena Caña, Juan Pedro|||0000-0002-3476-6261, Usero Aragonés, Luis|||0000-0001-8658-9992, Patricio Guisado, Miguel Ángel
Formato: artículo
Fecha de publicación:2024
País:España
Recursos:Universidad de Alcalá (UAH)
Repositorio:e_Buah Biblioteca Digital Universidad de Alcalá
Idioma:inglés
OAI Identifier:oai:ebuah.uah.es:10017/64489
Acesso em linha:http://hdl.handle.net/10017/64489
https://dx.doi.org/10.3390/app15010109
Access Level:acceso abierto
Palavra-chave:Unmanned aerial vehicles (UAVs)
Convolutional neural networks (CNNs)
Transformers (TNNs)
Thermal images
Informática
Computer science
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spelling A comparative study of convolutional neural network and transformer architectures for drone detection in thermal imagesGutiérrez, GianLlerena Caña, Juan Pedro|||0000-0002-3476-6261Usero Aragonés, Luis|||0000-0001-8658-9992Patricio Guisado, Miguel ÁngelUnmanned aerial vehicles (UAVs)Convolutional neural networks (CNNs)Transformers (TNNs)Thermal imagesInformáticaComputer scienceThe widespread growth of drone technology is generating new security paradigms, especially with regard to the unauthorized activities of UAVs in restricted or sensitive areas, as well as illegal and illicit activities or attacks. Among the various UAV detection technologies, vision systems in different spectra are postulated as outstanding technologies due to their peculiarities compared to other technologies. However, drone detection in thermal imaging is a challenging task due to specific factors such as thermal noise, temperature variability, or cluttered environments. This study addresses these challenges through a comparative evaluation of contemporary neural network architectures—specifically, convolutional neural networks (CNNs) and transformer-based models—for UAV detection in infrared imagery. The research focuses on real-world conditions and examines the performance of YOLOv9, GELAN, DETR, and ViTDet in different scenarios of the Anti-UAV Challenge 2023 dataset. The results show that YOLOv9 stands out for its real-time detection speed, while GELAN provides the highest accuracy in varying conditions and DETR performs reliably in thermally complex environments. The study contributes to the advancement of state-of-the-art UAV detection techniques and highlights the need for the further development of specialized models for specific detection scenarios.Agencia Estatal de InvestigaciónMDPI20242024-12-27journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10017/64489https://dx.doi.org/10.3390/app15010109reponame:e_Buah Biblioteca Digital Universidad de Alcaláinstname:Universidad de Alcalá (UAH)InglésengAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2020-118249RB-C22 CONCEPTOS DE VEHICULOS AEREOS EN LA CIUDAD: TRANSPORTE, URBANISMO Y SEGURIDADAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PDC2021-121567-C22 SIMBAT: SOLUTIONS FOR INTELLIGENT MONITORING BASED ON DRONE DATA AND AI TOOLSAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 TED2021-131520B-C22open accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:ebuah.uah.es:10017/644892026-06-18T11:13:07Z
dc.title.none.fl_str_mv A comparative study of convolutional neural network and transformer architectures for drone detection in thermal images
title A comparative study of convolutional neural network and transformer architectures for drone detection in thermal images
spellingShingle A comparative study of convolutional neural network and transformer architectures for drone detection in thermal images
Gutiérrez, Gian
Unmanned aerial vehicles (UAVs)
Convolutional neural networks (CNNs)
Transformers (TNNs)
Thermal images
Informática
Computer science
title_short A comparative study of convolutional neural network and transformer architectures for drone detection in thermal images
title_full A comparative study of convolutional neural network and transformer architectures for drone detection in thermal images
title_fullStr A comparative study of convolutional neural network and transformer architectures for drone detection in thermal images
title_full_unstemmed A comparative study of convolutional neural network and transformer architectures for drone detection in thermal images
title_sort A comparative study of convolutional neural network and transformer architectures for drone detection in thermal images
dc.creator.none.fl_str_mv Gutiérrez, Gian
Llerena Caña, Juan Pedro|||0000-0002-3476-6261
Usero Aragonés, Luis|||0000-0001-8658-9992
Patricio Guisado, Miguel Ángel
author Gutiérrez, Gian
author_facet Gutiérrez, Gian
Llerena Caña, Juan Pedro|||0000-0002-3476-6261
Usero Aragonés, Luis|||0000-0001-8658-9992
Patricio Guisado, Miguel Ángel
author_role author
author2 Llerena Caña, Juan Pedro|||0000-0002-3476-6261
Usero Aragonés, Luis|||0000-0001-8658-9992
Patricio Guisado, Miguel Ángel
author2_role author
author
author
dc.subject.none.fl_str_mv Unmanned aerial vehicles (UAVs)
Convolutional neural networks (CNNs)
Transformers (TNNs)
Thermal images
Informática
Computer science
topic Unmanned aerial vehicles (UAVs)
Convolutional neural networks (CNNs)
Transformers (TNNs)
Thermal images
Informática
Computer science
description The widespread growth of drone technology is generating new security paradigms, especially with regard to the unauthorized activities of UAVs in restricted or sensitive areas, as well as illegal and illicit activities or attacks. Among the various UAV detection technologies, vision systems in different spectra are postulated as outstanding technologies due to their peculiarities compared to other technologies. However, drone detection in thermal imaging is a challenging task due to specific factors such as thermal noise, temperature variability, or cluttered environments. This study addresses these challenges through a comparative evaluation of contemporary neural network architectures—specifically, convolutional neural networks (CNNs) and transformer-based models—for UAV detection in infrared imagery. The research focuses on real-world conditions and examines the performance of YOLOv9, GELAN, DETR, and ViTDet in different scenarios of the Anti-UAV Challenge 2023 dataset. The results show that YOLOv9 stands out for its real-time detection speed, while GELAN provides the highest accuracy in varying conditions and DETR performs reliably in thermally complex environments. The study contributes to the advancement of state-of-the-art UAV detection techniques and highlights the need for the further development of specialized models for specific detection scenarios.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-12-27
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10017/64489
https://dx.doi.org/10.3390/app15010109
url http://hdl.handle.net/10017/64489
https://dx.doi.org/10.3390/app15010109
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2020-118249RB-C22 CONCEPTOS DE VEHICULOS AEREOS EN LA CIUDAD: TRANSPORTE, URBANISMO Y SEGURIDAD
Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PDC2021-121567-C22 SIMBAT: SOLUTIONS FOR INTELLIGENT MONITORING BASED ON DRONE DATA AND AI TOOLS
Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 TED2021-131520B-C22
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/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 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 MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:e_Buah Biblioteca Digital Universidad de Alcalá
instname:Universidad de Alcalá (UAH)
instname_str Universidad de Alcalá (UAH)
reponame_str e_Buah Biblioteca Digital Universidad de Alcalá
collection e_Buah Biblioteca Digital Universidad de Alcalá
repository.name.fl_str_mv
repository.mail.fl_str_mv
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