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Fusion flow-enhanced graph pooling residual networks for unmanned aerial vehicles surveillance in day and night dual visions

2024Journal article Open access
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dc.contributor.authorNoor, Alam
dc.contributor.authorLi, Kai
dc.contributor.authorTovar, Eduardo
dc.contributor.authorZhang, Pei
dc.contributor.authorWei, Bo
dc.date.accessioned2024-11-20T13:50:57Z
dc.date.available2024-11-20T13:50:57Z
dc.date.issued2024
dc.descriptionThis work was supported by the CISTER Research Unit (UIDP/UIDB/04234/2020) and project ADANET (PTDC/EEICOM/3362/2021), financed by National Funds through FCT/MCTES (Portuguese Foundation for Science and Technology). Also, this article is a result of the project NORTE-01-0145-FEDER-000062 (RETINA), supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF).pt_PT
dc.description.abstractRecognizing unauthorized Unmanned Aerial Vehicles (UAVs) within designated no-fly zones throughout the day and night is of paramount importance, where the unauthorized UAVs pose a substantial threat to both civil and military aviation safety. However, recognizing UAVs day and night with dual-vision cameras is nontrivial, since red-green-blue (RGB) images suffer from a low detection rate under an insufficient light condition, such as on cloudy or stormy days, while black-and-white infrared (IR) images struggle to capture UAVs that overlap with the background at night. In this paper, we propose a new optical flow-assisted graph-pooling residual network (OF-GPRN), which significantly enhances the UAV detection rate in day and night dual visions. The proposed OF-GPRN develops a new optical fusion to remove superfluous backgrounds, which improves RGB/IR imaging clarity. Furthermore, OF-GPRN extends optical fusion by incorporating a graph residual split attention network and a feature pyramid, which refines the perception of UAVs, leading to a higher success rate in UAV detection. A comprehensive performance evaluation is conducted using a benchmark UAV catch dataset. The results indicate that the proposed OF-GPRN elevates the UAV mean average precision (mAP) detection rate to 87.8%, marking a 17.9% advancement compared to the residual graph neural network (ResGCN)-based approach.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationAlam Noor, Kai Li, Eduardo Tovar, Pei Zhang, Bo Wei, Fusion flow-enhanced graph pooling residual networks for Unmanned Aerial Vehicles surveillance in day and night dual visions, Engineering Applications of Artificial Intelligence, Volume 136, Part B, 2024, 108959, ISSN 0952-1976, https://doi.org/10.1016/j.engappai.2024.108959pt_PT
dc.identifier.doi10.1016/j.engappai.2024.108959pt_PT
dc.identifier.issn0952-1976
dc.identifier.urihttp://hdl.handle.net/10400.22/26423
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0952197624011175pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/pt_PT
dc.subjectUnmanned aerial vehicles surveillancept_PT
dc.subjectResidual convolutional networkspt_PT
dc.subjectSplit attention networkpt_PT
dc.subjectOptical flow fusionpt_PT
dc.titleFusion flow-enhanced graph pooling residual networks for unmanned aerial vehicles surveillance in day and night dual visionspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.titleEngineering Applications of Artificial Intelligencept_PT
oaire.citation.volume136pt_PT
person.familyNameLi
person.familyNameTovar
person.givenNameKai
person.givenNameEduardo
person.identifier.ciencia-idEE10-B822-16ED
person.identifier.ciencia-id6017-8881-11E8
person.identifier.orcid0000-0002-0517-2392
person.identifier.orcid0000-0001-8979-3876
person.identifier.scopus-author-id7006312557
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication21f3fb85-19c2-4c89-afcd-3acb27cedc5e
relation.isAuthorOfPublication80b63d8a-2e6d-484e-af3c-55849d0cb65e
relation.isAuthorOfPublication.latestForDiscovery21f3fb85-19c2-4c89-afcd-3acb27cedc5e

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