Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1116449
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dc.contributor.authorBARBEDO, J. G. A.eng
dc.contributor.authorKOENIGKAN, L. V.eng
dc.contributor.authorSANTOS, T. T.eng
dc.contributor.authorSANTOS, P. M.eng
dc.date.accessioned2019-12-10T18:21:42Z-
dc.date.available2019-12-10T18:21:42Z-
dc.date.created2019-12-10
dc.date.issued2019
dc.identifier.citationSensors, v. 19, n. 24, 5436, Dec. 2019.eng
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1116449-
dc.descriptionAbstract: Unmanned aerial vehicles (UAVs) are being increasingly viewed as valuable tools to aid the management of farms. This kind of technology can be particularly useful in the context of extensive cattle farming, as production areas tend to be expansive and animals tend to be more loosely monitored. With the advent of deep learning, and convolutional neural networks (CNNs) in particular, extracting relevant information from aerial images has become more effective. Despite the technological advancements in drone, imaging and machine learning technologies, the application of UAVs for cattle monitoring is far from being thoroughly studied, with many research gaps still remaining. In this context, the objectives of this study were threefold: (1) to determine the highest possible accuracy that could be achieved in the detection of animals of the Canchim breed, which is visually similar to the Nelore breed (Bos taurus indicus); (2) to determine the ideal ground sample distance (GSD) for animal detection; (3) to determine the most accurate CNN architecture for this specific problem. The experiments involved 1853 images containing 8629 samples of animals, and 15 different CNN architectures were tested. A total of 900 models were trained (15 CNN architectures 3 spacial resolutions 2 datasets 10-fold cross validation), allowing for a deep analysis of the several aspects that impact the detection of cattle using aerial images captured using UAVs. Results revealed that many CNN architectures are robust enough to reliably detect animals in aerial images even under far from ideal conditions, indicating the viability of using UAVs for cattle monitoring.eng
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectVeículo aéreo não tripuladoeng
dc.subjectRedes neuraiseng
dc.subjectDroneeng
dc.subjectAprendizado profundoeng
dc.subjectConvolutional neural networkseng
dc.subjectDeep learningeng
dc.subjectCanchim breedeng
dc.subjectNelore breedeng
dc.titleA study on the detection of cattle in UAV images using deep learning.eng
dc.typeArtigo de periódicoeng
dc.date.updated2019-12-10T18:21:42Z
dc.subject.thesagroGado de Corteeng
dc.subject.thesagroGado Canchimeng
dc.subject.thesagroGado Neloreeng
dc.subject.nalthesaurusCattleeng
dc.subject.nalthesaurusUnmanned aerial vehicleseng
dc.format.extent214 p.eng
riaa.ainfo.id1116449eng
riaa.ainfo.lastupdate2019-12-10
dc.identifier.doi10.3390/s19245436eng
dc.contributor.institutionJAYME GARCIA ARNAL BARBEDO, CNPTIA; LUCIANO VIEIRA KOENIGKAN, CNPTIA; THIAGO TEIXEIRA SANTOS, CNPTIA; PATRICIA MENEZES SANTOS, CPPSE.eng
Aparece nas coleções:Artigo em periódico indexado (CNPTIA)

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