A Hybrid Deep Learning Model for UAVs Detection in Day and Night Dual Visions

Detalhes bibliográficos
Autor(a) principal: Noor, Alam
Data de Publicação: 2023
Outros Autores: Li, Kai, Ammar, Adel, Koubâa, Anis, Benjdira, Bilel, Tovar, Eduardo
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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spelling 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
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