Data/hora: 06/06/2024 09:15:31

Biblioteca(s): 	Embrapa Agricultura Digital; Embrapa Pecuária Sudeste.	
Data corrente: 	08/12/2020	
Data da última atualização: 	09/12/2020	
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.	
Título: 	Cattle detection using oblique UAV images.	
Ano de publicação: 	2020	
Fonte/Imprenta: 	Drones, v. 4, n. 4, p. 1-9, Dec. 2020.	
DOI: 	10.3390/drones4040075	
Idioma: 	Inglês	
Notas: 	Article 75.	
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.	
Palavras-Chave: 	Aprendizado profundo; Convolutional neural network; Deep learning; Redes neurais; Redes neurais convolucionais; Veículos aéreos não tripulados.	
Thesagro: 	Gado.	
Thesaurus Nal: 	Cattle; Unmanned aerial vehicles.	
Categoria do assunto: 	X Pesquisa, Tecnologia e Engenharia	
URL: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/item/218988/1/AP-Cattle-detection-Drones-2020.pdf	
Registro original: 	Embrapa Agricultura Digital (CNPTIA)	
Exemplares: 	CPPSE - (25222-1) - UPC - AP - DD - PROCI-2020.00130 - BAR - 2020.00181 - DIS
CNPTIA - (20817-1) - UPC - AP - DD -  -  -  - DIS