Título:	Cattle detection using oblique UAV images.
Autoria:	BARBEDO, J. G. A. KOENIGKAN, L. V. SANTOS, P. M.
Afiliação:	JAYME GARCIA ARNAL BARBEDO, CNPTIA; LUCIANO VIEIRA KOENIGKAN, CNPTIA; PATRICIA MENEZES SANTOS, CPPSE.
Ano de publicação:	2020
Referência:	Drones, v. 4, n. 4, p. 1-9, Dec. 2020.
Conteúdo:	The evolution in imaging technologies and artificial intelligence algorithms, coupled with improvements in UAV technology, has enabled the use of unmanned aircraft in a wide range of applications. The feasibility of this kind of approach for cattle monitoring has been demonstrated by several studies, but practical use is still challenging due to the particular characteristics of this application, such as the need to track mobile targets and the extensive areas that need to be covered in most cases. The objective of this study was to investigate the feasibility of using a tilted angle to increase the area covered by each image. Deep Convolutional Neural Networks (Xception architecture) were used to generate the models for animal detection. Three experiments were carried out: (1) five different sizes for the input images were tested to determine which yields the highest accuracies; (2) detection accuracies were calculated for different distances between animals and sensor, in order to determine how distance influences detectability; and (3) animals that were completely missed by the detection process were individually identified and the cause for those errors were determined, revealing some potential topics for further research. Experimental results indicate that oblique images can be successfully used under certain conditions, but some practical limitations need to be addressed in order to make this approach appealing.
Thesagro:	Gado
NAL Thesaurus:	Unmanned aerial vehicles Cattle
Palavras-chave:	Redes neurais Redes neurais convolucionais Aprendizado profundo Veículos aéreos não tripulados Convolutional neural network Deep learning
Digital Object Identifier:	10.3390/drones4040075
Notas:	Article 75.
Tipo do material:	Artigo de periódico
Acesso:	openAccess
Aparece nas coleções:	Artigo em periódico indexado (CNPTIA)