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UAV Landing Using Computer Vision Techniques for Human Detection

2020Journal article Open access
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dc.contributor.authorSafadinho, David
dc.contributor.authorRamos, João
dc.contributor.authorRibeiro, Roberto
dc.contributor.authorFilipe, Vítor
dc.contributor.authorBarroso, João
dc.contributor.authorPereira, António
dc.date.accessioned2021-07-29T11:01:40Z
dc.date.available2021-07-29T11:01:40Z
dc.date.issued2020
dc.description.abstractThe capability of drones to perform autonomous missions has led retail companies to use them for deliveries, saving time and human resources. In these services, the delivery depends on the Global Positioning System (GPS) to define an approximate landing point. However, the landscape can interfere with the satellite signal (e.g., tall buildings), reducing the accuracy of this approach. Changes in the environment can also invalidate the security of a previously defined landing site (e.g., irregular terrain, swimming pool). Therefore, the main goal of this work is to improve the process of goods delivery using drones, focusing on the detection of the potential receiver. We developed a solution that has been improved along its iterative assessment composed of five test scenarios. The built prototype complements the GPS through Computer Vision (CV) algorithms, based on Convolutional Neural Networks (CNN), running in a Raspberry Pi 3 with a Pi NoIR Camera (i.e., No InfraRed-without infrared filter). The experiments were performed with the models Single Shot Detector (SSD) MobileNet-V2, and SSDLite-MobileNet-V2. The best results were obtained in the afternoon, with the SSDLite architecture, for distances and heights between 2.5-10 m, with recalls from 59%-76%. The results confirm that a low computing power and cost-effective system can perform aerial human detection, estimating the landing position without an additional visual marker.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationSafadinho, D.; Ramos, J.; Ribeiro, R.; Filipe, V.; Barroso, J.; Pereira, A. UAV Landing Using Computer Vision Techniques for Human Detection. Sensors 2020, 20, 613. https://doi.org/10.3390/s20030613pt_PT
dc.identifier.doi10.3390/s20030613pt_PT
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/10400.8/5967
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectAutonomous deliverypt_PT
dc.subjectComputer visionpt_PT
dc.subjectDeep neural networkspt_PT
dc.subjectIntelligent vehiclespt_PT
dc.subjectInternet of thingspt_PT
dc.subjectNext generation servicespt_PT
dc.subjectReal-time systemspt_PT
dc.subjectRemote sensingpt_PT
dc.subjectUnmanned aerial vehiclespt_PT
dc.subjectUnmanned aircraft systemspt_PT
dc.titleUAV Landing Using Computer Vision Techniques for Human Detectionpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue3pt_PT
oaire.citation.startPage613pt_PT
oaire.citation.titleSensorspt_PT
oaire.citation.volume20pt_PT
person.familyNameSafadinho
person.familyNameRamos
person.familyNameRibeiro
person.familyNameJesus Filipe
person.familyNameBarroso
person.familyNamePereira
person.givenNameDavid
person.givenNameJoão
person.givenNameRoberto
person.givenNameVítor Manuel
person.givenNameJoão
person.givenNameAntónio
person.identifier.ciencia-id5718-5FDF-6FE1
person.identifier.ciencia-id8417-9F61-D162
person.identifier.ciencia-id7F12-40BA-57CD
person.identifier.ciencia-idE716-23C3-FAFF
person.identifier.ciencia-idED10-59A9-4E28
person.identifier.ciencia-idE215-4F0F-33EC
person.identifier.orcid0000-0001-5700-7893
person.identifier.orcid0000-0001-5361-9809
person.identifier.orcid0000-0003-1547-4674
person.identifier.orcid0000-0002-3747-6577
person.identifier.orcid0000-0003-4847-5104
person.identifier.orcid0000-0001-5062-1241
person.identifier.ridI-5130-2014
person.identifier.ridM-6163-2013
person.identifier.scopus-author-id20435746800
person.identifier.scopus-author-id7402230199
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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