Deep learning for small object detection

dc.contributor.advisorMucientes Molina, Manuel
dc.contributor.advisorBrea Sánchez, Víctor Manuel
dc.contributor.affiliationUniversidade de Santiago de Compostela. Escola de Doutoramento Internacional (EDIUS)
dc.contributor.authorBosquet Mera, Brais
dc.date.accessioned2021-02-16T09:17:43Z
dc.date.available2021-12-18T02:00:09Z
dc.date.issued2020
dc.description.abstractSmall object detection has become increasingly relevant due to the fact that the performance of common object detectors falls significantly as objects become smaller. Many computer vision applications require the analysis of the entire set of objects in the image, including extremely small objects. Moreover, the detection of small objects allows to perceive objects at a greater distance, thus giving more time to adapt to any situation or unforeseen event.gl
dc.description.programaUniversidade de Santiago de Compostela. Programa de Doutoramento en Investigación en Tecnoloxías da Información
dc.identifier.urihttp://hdl.handle.net/10347/24470
dc.language.isoenggl
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional
dc.rights.accessRightsopen accessgl
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectsmall object detectiongl
dc.subjectconvolutional neural networks (CNNs)gl
dc.subjectgenerative adversarial networks (GANs)gl
dc.subjectdeep learninggl
dc.subjectspatio-temporal convolutional networkgl
dc.subjectdata augmentationgl
dc.subjectobject linkingl
dc.subject.classificationMaterias::Investigación::12 Matemáticas::1203 Ciencia de los ordenadores::120304 Inteligencia artificialgl
dc.titleDeep learning for small object detectiongl
dc.typedoctoral thesisgl
dspace.entity.typePublication
relation.isAdvisorOfPublication21112b72-72a3-4a96-bda4-065e7e2bb262
relation.isAdvisorOfPublication22d4aeb8-73ba-4743-a84e-9118799ab1f2
relation.isAdvisorOfPublication.latestForDiscovery21112b72-72a3-4a96-bda4-065e7e2bb262

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