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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital; Embrapa Pecuária Sudeste. |
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Data corrente: |
08/12/2020 |
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Data da última atualização: |
09/12/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
BARBEDO, J. G. A.; KOENIGKAN, L. V.; SANTOS, P. M. |
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Afiliação: |
JAYME GARCIA ARNAL BARBEDO, CNPTIA; LUCIANO VIEIRA KOENIGKAN, CNPTIA; PATRICIA MENEZES SANTOS, CPPSE. |
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Título: |
Cattle detection using oblique UAV images. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Drones, v. 4, n. 4, p. 1-9, Dec. 2020. |
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DOI: |
10.3390/drones4040075 |
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Idioma: |
Inglês |
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Notas: |
Article 75. |
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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. |
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Palavras-Chave: |
Aprendizado profundo; Convolutional neural network; Deep learning; Redes neurais; Redes neurais convolucionais; Veículos aéreos não tripulados. |
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Thesagro: |
Gado. |
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Thesaurus Nal: |
Cattle; Unmanned aerial vehicles. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/218988/1/AP-Cattle-detection-Drones-2020.pdf
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Marc: |
LEADER 02229naa a2200277 a 4500
001 2127885
005 2020-12-09
008 2020 bl uuuu u00u1 u #d
024 7 $a10.3390/drones4040075$2DOI
100 1 $aBARBEDO, J. G. A.
245 $aCattle detection using oblique UAV images.$h[electronic resource]
260 $c2020
500 $aArticle 75.
520 $aThe 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.
650 $aCattle
650 $aUnmanned aerial vehicles
650 $aGado
653 $aAprendizado profundo
653 $aConvolutional neural network
653 $aDeep learning
653 $aRedes neurais
653 $aRedes neurais convolucionais
653 $aVeículos aéreos não tripulados
700 1 $aKOENIGKAN, L. V.
700 1 $aSANTOS, P. M.
773 $tDrones$gv. 4, n. 4, p. 1-9, Dec. 2020.
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Embrapa Agricultura Digital (CNPTIA) |
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