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Artificial intelligence architecture based on planar LIDAR scan data to detect energy pylon structures in a UAV autonomous detailed inspection process

2021Conference paper Open access
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dc.contributor.authorFerraz, Matheus
dc.contributor.authorJúnior, Luciano B.
dc.contributor.authorKomori, Aroldo S.K.
dc.contributor.authorRech, Lucas C.
dc.contributor.authorSchneider, Guilherme H.T.
dc.contributor.authorBerger, Guido
dc.contributor.authorCantieri, Álvaro R.
dc.contributor.authorLima, José
dc.contributor.authorWehrmeister, Marco A.
dc.date.accessioned2022-01-13T13:41:39Z
dc.date.available2022-01-13T13:41:39Z
dc.date.issued2021
dc.description.abstractThe technological advances in Unmanned Aerial Vehicles (UAV) related to energy power structure inspection are gaining visibility in the past decade, due to the advantages of this technique compared with traditional inspection methods. In the particular case of power pylon structure and components, autonomous UAV inspection architectures are able to increase the efficacy and security of these tasks. This kind of application presents technical challenges that must be faced to build real-world solutions, especially the precise positioning and path following for the UAV during a mission. This paper aims to evaluate a novel architecture applied to a power line pylon inspection process, based on the machine learning techniques to process and identify the signal obtained from a UAV-embedded planar Light Detection and Ranging - LiDAR sensor. A simulated environment built on the GAZEBO software presents a first evaluation of the architecture. The results show an positive detection accuracy level superior to 97% using the vertical scan data and 70% using the horizontal scan data. This accuracy level indicates that the proposed architecture is proper for the development of positioning algorithms based on the LiDAR scan data of a power pylon.pt_PT
dc.description.sponsorshipThis work has been supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UIDB/05757/2020. This work has also been supported by Fundação Araucária (grant 34/2019), and by CAPES and UTFPR through stundent scholarships.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationFerraz, Matheus F.; Júnior, Luciano B.; Komori, Aroldo S.K.; Rech, Lucas C.; Schneider, Guilherme H.T.; Berger, Guido S.; Cantieri, Álvaro R.; Lima, José; Wehrmeister, Marco A. (2021). Artificial intelligence architecture based on planar lidar scan data to detect energy pylon structures in a UAV autonomous detailed inspection process. In International Conference on Optimization, Learning Algorithms and Applications, OL2A 2021. p. 430-443. ISBN 978-303091884-2pt_PT
dc.identifier.doi10.1007/978-3-030-91885-9_32pt_PT
dc.identifier.isbn978-303091884-2
dc.identifier.urihttp://hdl.handle.net/10198/24620
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectUAV LiDAR pylon detectionpt_PT
dc.subjectDetailed electric pylon inspectionpt_PT
dc.subjectMachine learning pylon detectionpt_PT
dc.titleArtificial intelligence architecture based on planar LIDAR scan data to detect energy pylon structures in a UAV autonomous detailed inspection processpt_PT
dc.typeconference paper
dspace.entity.typePublication
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.citation.endPage443pt_PT
oaire.citation.startPage430pt_PT
oaire.citation.volume1488pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameBerger
person.familyNameLima
person.givenNameGuido
person.givenNameJosé
person.identifierR-000-8GD
person.identifier.ciencia-id931A-887B-D75D
person.identifier.ciencia-id6016-C902-86A9
person.identifier.orcid0000-0002-4100-1494
person.identifier.orcid0000-0001-7902-1207
person.identifier.ridL-3370-2014
person.identifier.scopus-author-id55851941311
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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