A Hybrid Deep Learning Model for UAVs Detection in Day and Night Dual Visions
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.22/24094 |
Resumo: | Unmanned Aerial Vehicle (UAV) detection for public safety protection is becoming a critical issue in non-fly zones. There are plenty of attempts of the UAV detection using single stream (day or night vision). In this paper, we propose a new hybrid deep learning model to detect the UAVs in day and night visions with a high detection precision and accurate bounding box localization. The proposed hybrid deep learning model is developed with cosine annealing and rethinking transformation to improve the detection precision and accelerate the training convergence. To validate the hybrid deep learning model, real-world experiments are conducted outdoor in daytime and nighttime, where a surveillance video camera on the ground is set up for capturing the UAV. In addition, the UAV-Catch open database is adopted for offline training of the proposed hybrid model, which enriches training datasets and improves the detection precision. The experimental results show that the proposed hybrid deep learning model achieves 65% in terms of the mean average detection precision given the input videos in day and night visions. |
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A Hybrid Deep Learning Model for UAVs Detection in Day and Night Dual Visions211103Unmanned Aerial Vehicle (UAV) detection for public safety protection is becoming a critical issue in non-fly zones. There are plenty of attempts of the UAV detection using single stream (day or night vision). In this paper, we propose a new hybrid deep learning model to detect the UAVs in day and night visions with a high detection precision and accurate bounding box localization. The proposed hybrid deep learning model is developed with cosine annealing and rethinking transformation to improve the detection precision and accelerate the training convergence. To validate the hybrid deep learning model, real-world experiments are conducted outdoor in daytime and nighttime, where a surveillance video camera on the ground is set up for capturing the UAV. In addition, the UAV-Catch open database is adopted for offline training of the proposed hybrid model, which enriches training datasets and improves the detection precision. The experimental results show that the proposed hybrid deep learning model achieves 65% in terms of the mean average detection precision given the input videos in day and night visions.Repositório Científico do Instituto Politécnico do PortoNoor, AlamLi, KaiAmmar, AdelKoubâa, AnisBenjdira, BilelTovar, Eduardo2023-12-07T11:00:59Z2023-12-012023-12-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/24094engmetadata only accessinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-12-13T01:50:45Zoai:recipp.ipp.pt:10400.22/24094Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:42:18.401897Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
A Hybrid Deep Learning Model for UAVs Detection in Day and Night Dual Visions 211103 |
title |
A Hybrid Deep Learning Model for UAVs Detection in Day and Night Dual Visions |
spellingShingle |
A Hybrid Deep Learning Model for UAVs Detection in Day and Night Dual Visions Noor, Alam |
title_short |
A Hybrid Deep Learning Model for UAVs Detection in Day and Night Dual Visions |
title_full |
A Hybrid Deep Learning Model for UAVs Detection in Day and Night Dual Visions |
title_fullStr |
A Hybrid Deep Learning Model for UAVs Detection in Day and Night Dual Visions |
title_full_unstemmed |
A Hybrid Deep Learning Model for UAVs Detection in Day and Night Dual Visions |
title_sort |
A Hybrid Deep Learning Model for UAVs Detection in Day and Night Dual Visions |
author |
Noor, Alam |
author_facet |
Noor, Alam Li, Kai Ammar, Adel Koubâa, Anis Benjdira, Bilel Tovar, Eduardo |
author_role |
author |
author2 |
Li, Kai Ammar, Adel Koubâa, Anis Benjdira, Bilel Tovar, Eduardo |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Politécnico do Porto |
dc.contributor.author.fl_str_mv |
Noor, Alam Li, Kai Ammar, Adel Koubâa, Anis Benjdira, Bilel Tovar, Eduardo |
description |
Unmanned Aerial Vehicle (UAV) detection for public safety protection is becoming a critical issue in non-fly zones. There are plenty of attempts of the UAV detection using single stream (day or night vision). In this paper, we propose a new hybrid deep learning model to detect the UAVs in day and night visions with a high detection precision and accurate bounding box localization. The proposed hybrid deep learning model is developed with cosine annealing and rethinking transformation to improve the detection precision and accelerate the training convergence. To validate the hybrid deep learning model, real-world experiments are conducted outdoor in daytime and nighttime, where a surveillance video camera on the ground is set up for capturing the UAV. In addition, the UAV-Catch open database is adopted for offline training of the proposed hybrid model, which enriches training datasets and improves the detection precision. The experimental results show that the proposed hybrid deep learning model achieves 65% in terms of the mean average detection precision given the input videos in day and night visions. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-12-07T11:00:59Z 2023-12-01 2023-12-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.22/24094 |
url |
http://hdl.handle.net/10400.22/24094 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
metadata only access info:eu-repo/semantics/openAccess |
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metadata only access |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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