Application of Deep-Learning Methods to Bird Detection Using Unmanned Aerial Vehicle Imagery.

Hong, Suk-Ju; Han, Yunhyeok; Kim, Sang-Yeon; Lee, Ah-Yeong; Kim, Ghiseok

Hong, Suk-Ju; Han, Yunhyeok; Kim, Sang-Yeon; Lee, Ah-Yeong; Kim, Ghiseok.

Afiliação

Hong SJ; Department of Biosystems and Biomaterials Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea. hsj5596@snu.ac.kr.
Han Y; Department of Biosystems and Biomaterials Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea. redstar316@snu.ac.kr.
Kim SY; Department of Biosystems and Biomaterials Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea. yskra@snu.ac.kr.
Lee AY; Department of Biosystems and Biomaterials Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea. lay117@korea.kr.
Kim G; National Institute of Agricultural Sciences, Rural Development Administration, Jeollabuk-do 54875, Korea. lay117@korea.kr.

Sensors (Basel) ; 19(7)2019 Apr 06.

Article em En | MEDLINE | ID: mdl-30959913

Assuntos

Aves; Aprendizado Profundo; Tecnologia de Sensoriamento Remoto/métodos; Animais; Aprendizado de Máquina; Redes Neurais de Computação

Palavras-chave

bird detection; convolutional neural networks; deep learning; unmanned aerial vehicle

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Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Aves / Tecnologia de Sensoriamento Remoto / Aprendizado Profundo Tipo de estudo: Diagnostic_studies Limite: Animals Idioma: En Revista: Sensors (Basel) Ano de publicação: 2019 Tipo de documento: Article

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