Detection of Cattle Using Drones and Convolutional Neural Networks

[EN] Multirotor drones have been one of the most important technological advances of the last decade. Their mechanics are simple compared to other types of drones and their possibilities in flight are greater. For example, they can take-off vertically. Their capabilities have therefore brought progr...

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Detalles Bibliográficos
Autores: Rivas Camacho, Alberto, Chamoso Santos, Pablo, González Briones, Alfonso, Corchado Rodríguez, Juan Manuel
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2018
País:España
Institución:Universidad de Salamanca (USAL)
Repositorio:GREDOS. Repositorio Institucional de la Universidad de Salamanca
OAI Identifier:oai:gredos.usal.es:10366/145836
Acceso en línea:http://hdl.handle.net/10366/145836
Access Level:acceso abierto
Palabra clave:Cattle detection
Convolutional neural network
Multirotor
Drone
Unmanned Aerial Vehicle
1203.17 Informática
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spelling Detection of Cattle Using Drones and Convolutional Neural NetworksRivas Camacho, AlbertoChamoso Santos, PabloGonzález Briones, AlfonsoCorchado Rodríguez, Juan ManuelCattle detectionConvolutional neural networkMultirotorDroneUnmanned Aerial Vehicle1203.17 Informática[EN] Multirotor drones have been one of the most important technological advances of the last decade. Their mechanics are simple compared to other types of drones and their possibilities in flight are greater. For example, they can take-off vertically. Their capabilities have therefore brought progress to many professional activities. Moreover, advances in computing and telecommunications have also broadened the range of activities in which drones may be used. Currently, artificial intelligence and information analysis are the main areas of research in the field of computing. The case study presented in this article employed artificial intelligence techniques in the analysis of information captured by drones. More specifically, the camera installed in the drone took images which were later analyzed using Convolutional Neural Networks (CNNs) to identify the objects captured in the images. In this research, a CNN was trained to detect cattle, however the same training process could be followed to develop a CNN for the detection of any other object. This article describes the design of the platform for real-time analysis of information and its performance in the detection of cattle.202120212018info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10366/145836reponame:GREDOS. Repositorio Institucional de la Universidad de Salamancainstname:Universidad de Salamanca (USAL)InglésAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:gredos.usal.es:10366/1458362026-06-07T06:28:51Z
dc.title.none.fl_str_mv Detection of Cattle Using Drones and Convolutional Neural Networks
title Detection of Cattle Using Drones and Convolutional Neural Networks
spellingShingle Detection of Cattle Using Drones and Convolutional Neural Networks
Rivas Camacho, Alberto
Cattle detection
Convolutional neural network
Multirotor
Drone
Unmanned Aerial Vehicle
1203.17 Informática
title_short Detection of Cattle Using Drones and Convolutional Neural Networks
title_full Detection of Cattle Using Drones and Convolutional Neural Networks
title_fullStr Detection of Cattle Using Drones and Convolutional Neural Networks
title_full_unstemmed Detection of Cattle Using Drones and Convolutional Neural Networks
title_sort Detection of Cattle Using Drones and Convolutional Neural Networks
dc.creator.none.fl_str_mv Rivas Camacho, Alberto
Chamoso Santos, Pablo
González Briones, Alfonso
Corchado Rodríguez, Juan Manuel
author Rivas Camacho, Alberto
author_facet Rivas Camacho, Alberto
Chamoso Santos, Pablo
González Briones, Alfonso
Corchado Rodríguez, Juan Manuel
author_role author
author2 Chamoso Santos, Pablo
González Briones, Alfonso
Corchado Rodríguez, Juan Manuel
author2_role author
author
author
dc.subject.none.fl_str_mv Cattle detection
Convolutional neural network
Multirotor
Drone
Unmanned Aerial Vehicle
1203.17 Informática
topic Cattle detection
Convolutional neural network
Multirotor
Drone
Unmanned Aerial Vehicle
1203.17 Informática
description [EN] Multirotor drones have been one of the most important technological advances of the last decade. Their mechanics are simple compared to other types of drones and their possibilities in flight are greater. For example, they can take-off vertically. Their capabilities have therefore brought progress to many professional activities. Moreover, advances in computing and telecommunications have also broadened the range of activities in which drones may be used. Currently, artificial intelligence and information analysis are the main areas of research in the field of computing. The case study presented in this article employed artificial intelligence techniques in the analysis of information captured by drones. More specifically, the camera installed in the drone took images which were later analyzed using Convolutional Neural Networks (CNNs) to identify the objects captured in the images. In this research, a CNN was trained to detect cattle, however the same training process could be followed to develop a CNN for the detection of any other object. This article describes the design of the platform for real-time analysis of information and its performance in the detection of cattle.
publishDate 2018
dc.date.none.fl_str_mv 2018
2021
2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10366/145836
url http://hdl.handle.net/10366/145836
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:GREDOS. Repositorio Institucional de la Universidad de Salamanca
instname:Universidad de Salamanca (USAL)
instname_str Universidad de Salamanca (USAL)
reponame_str GREDOS. Repositorio Institucional de la Universidad de Salamanca
collection GREDOS. Repositorio Institucional de la Universidad de Salamanca
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300719

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