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dc.contributor.authorTran, Duy Khoi
dc.contributor.authorNguyen, van Nhan
dc.contributor.authorRoverso, Davide
dc.contributor.authorJenssen, Robert
dc.contributor.authorKampffmeyer, Michael Christian
dc.date.accessioned2024-10-11T10:48:59Z
dc.date.available2024-10-11T10:48:59Z
dc.date.issued2024-03-24
dc.description.abstractThis paper addresses the crucial task of power line detection and localization in electrical infrastructure inspection using Unmanned Aerial Vehicles (UAVs) from weak supervision, polyline annotations. We first identify several limitations in the state-of-the-art approach LSNet. In particular, the inability of LSNet to detect line-crossings and lines in close proximity. To overcome these limitations, we propose LSNetv2, which enhances LSNet with multi-line segment detection capability facilitated via a bipartite matching loss. Additionally, we update LSNet’s regression loss in order to stabilize training by reducing the interdependence between predicted coordinates. Finally, LSNetv2 makes use of an increased receptive field to extract global information, improving overall detection performance. Through extensive evaluations on various power line detection datasets, LSNetv2 demonstrates superior performance and robustness. On the public datasets PLDU, PLDM and TTPLA, it achieved 𝐹�𝛽� scores of 0.857, 0.875, and 0.671, respectively, while using only modified weak polyline annotation, establishing itself as an effective and efficient solution for power line detection in UAV-based electrical infrastructure inspections.en_US
dc.identifier.citationTran, Nguyen, Roverso, Jenssen, Kampffmeyer. LSNetv2: Improving weakly supervised power line detection with bipartite matching. Expert Systems With Applications. 2024;250en_US
dc.identifier.cristinIDFRIDAID 2263807
dc.identifier.doi10.1016/j.eswa.2024.123773
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.urihttps://hdl.handle.net/10037/35198
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.journalExpert Systems With Applications
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2024 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleLSNetv2: Improving weakly supervised power line detection with bipartite matchingen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's license is described as Attribution 4.0 International (CC BY 4.0)