Publicación:
UAV Landing Platform Recognition Using Cognitive Computation Combining Geometric Analysis and Computer Vision Techniques

Vista sencilla de ítem
dc.contributor.authorGarcía Pulido, José Antonio
dc.contributor.authorPajares, Gonzalo
dc.contributor.authorDormido Canto, Sebastián
dc.date.accessioned2024-10-02T14:20:32Z
dc.date.available2024-10-02T14:20:32Z
dc.date.issued2022-06-08
dc.descriptionThe registered version of this article, first published in Cognitive Computation, is available online at the publisher's website: Springer, https://doi.org/10.1007/s12559-021-09962-2
dc.descriptionLa versión registrada de este artículo, publicado por primera vez en Cognitive Computation, está disponible en línea en el sitio web del editor: Springer, https://doi.org/10.1007/s12559-021-09962-2
dc.description.abstractBACKGROUND/INTRODUCTION: Unmanned Aerial Vehicles (UAVs) are excellent tools with extensive demand. During the last phase of landing, they require additional support to that of than GPS. This can be achieved through the UAV’s perception system based on its on-board camera and intelligence, and with which decisions can be made as to how to land on a platform (target). METHODS: A cognitive computation approach is proposed to recognize this target that has been specifically designed to translate human reasoning into a computational procedures by computing two probabilities of detection which are combined considering the fuzzy set theory for proper decision-making. The platform design is based on: 1) spectral information in the visible range which are uncommon colors in the UAV’s operating environments (indoors and outdoors), and 2) specific figures in the foreground, which allow partial perception of each figure. We exploit color image properties from specific-colored figures embedded on the platform, and which are identified by applying image processing and pattern recognition techniques, including Euclidean Distance Smart Geometric Analysis, to identify the platform in a very efficient and reliable manner. RESULTS: The test strategy uses 800 images captured with a smartphone onboard a quad-rotor UAV. The results verify the proposed method outperforms existing strategies, especially those that do not use color information. CONCLUSIONS: Platform recognition is also possible even with only a partial view of the target, due to image capture under adverse conditions. This demonstrates the effectiveness and robustness of the proposed cognitive computing-based perception system.en
dc.description.versionversión final
dc.identifier.citationGarcía-Pulido, J.A., Pajares, G. & Dormido, S. UAV Landing Platform Recognition Using Cognitive Computation Combining Geometric Analysis and Computer Vision Techniques. Cogn Comput 15, 392–412 (2023). https://doi.org/10.1007/s12559-021-09962-2
dc.identifier.doihttps://doi.org/10.1007/s12559-021-09962-2
dc.identifier.issn1866-9964
dc.identifier.urihttps://hdl.handle.net/20.500.14468/23869
dc.journal.titleCognitive Computation
dc.journal.volume15
dc.language.isoen
dc.page.final412
dc.page.initial392
dc.publisherSpringer
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentInformática y Automática
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.licenseAtribución 4.0 Internacional
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject33 Ciencias Tecnológicas
dc.subject.keywordsimage color segmentationen
dc.subject.keywordslanding platformen
dc.subject.keywordsUAVen
dc.subject.keywordspattern recognitionen
dc.subject.keywordsdecision-makingen
dc.subject.keywordsrecognition probabilityen
dc.subject.keywordsperception systemen
dc.subject.keywordsartificial intelligenceen
dc.subject.keywordscognitive computationen
dc.titleUAV Landing Platform Recognition Using Cognitive Computation Combining Geometric Analysis and Computer Vision Techniquesen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
Archivos
Bloque original
Mostrando 1 - 1 de 1
Miniatura
Nombre:
GarciaPulido_JoseAntonio_RecognitionUAVPlatform
Tamaño:
2.33 MB
Formato:
Adobe Portable Document Format
Descargar
Bloque de licencias
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
license.txt
Tamaño:
3.62 KB
Formato:
Item-specific license agreed to upon submission
Descripción:
Descargar
Colecciones
Artículos y papers