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dc.contributor.authorBARBEDO, J. G. A.eng
dc.contributor.authorKOENIGKAN, L. V.eng
dc.contributor.authorSANTOS, P. M.eng
dc.contributor.authorRIBEIRO, A. R. B.eng
dc.date.accessioned2020-04-16T01:02:40Z-
dc.date.available2020-04-16T01:02:40Z-
dc.date.created2020-04-15
dc.date.issued2020
dc.identifier.citationSensors, v. 20, n. 7, p. 1-14, Apr. 2020.eng
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1121664-
dc.descriptionAbstract: The management of livestock in extensive production systems may be challenging, especially in large areas. Using Unmanned Aerial Vehicles (UAVs) to collect images from the area of interest is quickly becoming a viable alternative, but suitable algorithms for extraction of relevant information from the images are still rare. This article proposes a method for counting cattle which combines a deep learning model for rough animal location, color space manipulation to increase contrast between animals and background, mathematical morphology to isolate the animals and infer the number of individuals in clustered groups, and image matching to take into account image overlap. Using Nelore and Canchim breeds as a case study, the proposed approach yields accuracies over 90% under a wide variety of conditions and backgrounds.eng
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectRedes neuraiseng
dc.subjectRede neural convolucionaleng
dc.subjectVeículo aéreo não tripuladoeng
dc.subjectCanchim breedeng
dc.subjectNelore breedeng
dc.subjectConvolutional neural networkseng
dc.subjectMathematical morphologyeng
dc.subjectDeep learning modeeng
dc.titleCounting cattle in UAV images: dealing with clustered animals and animal/background contrast changes.eng
dc.typeArtigo de periódicoeng
dc.date.updated2020-04-17T11:11:11Z
dc.subject.thesagroGado de Corteeng
dc.subject.thesagroGado Neloreeng
dc.subject.thesagroGado Canchimeng
dc.subject.nalthesaurusUnmanned aerial vehicleseng
dc.subject.nalthesaurusNeural networkseng
dc.description.notesArticle number: 2126.eng
riaa.ainfo.id1121664eng
riaa.ainfo.lastupdate2020-04-17 -03:00:00
dc.identifier.doi10.3390/s20072126eng
dc.contributor.institutionJAYME GARCIA ARNAL BARBEDO, CNPTIA; LUCIANO VIEIRA KOENIGKAN, CNPTIA; PATRICIA MENEZES SANTOS, CPPSE; ANDREA ROBERTO BUENO RIBEIRO, UNISA; UNIP.eng
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