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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/45E3ET5
Repositorysid.inpe.br/sibgrapi/2021/09.13.10.35
Last Update2021:09.13.10.35.02 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2021/09.13.10.35.02
Metadata Last Update2022:09.10.00.16.17 (UTC) administrator
Citation KeySganderlaMaurSantPere:2021:DeClOb
TitleDetecção e Classificação de Objetos Presentes em Imagens Aéreas de Drones de Ambientes Urbanos
FormatOn-line
Year2021
Access Date2024, May 06
Number of Files1
Size1130 KiB
2. Context
Author1 Sganderla, Guilherme Rodrigues
2 Mauricio, Claudio Roberto Marquetto
3 Santos, Valéria Nunes dos
4 Peres, Fabiana Frata Frata
Affiliation1 Universidade Estadual do Oeste do Paraná
2 Universidade Estadual do Oeste do Paraná
3 Fundação Parque Tecnológico Itaipu
4 Universidade Estadual do Oeste do Paraná
EditorPaiva, Afonso
Menotti, David
Baranoski, Gladimir V. G.
Proença, Hugo Pedro
Junior, Antonio Lopes Apolinario
Papa, João Paulo
Pagliosa, Paulo
dos Santos, Thiago Oliveira
e Sá, Asla Medeiros
da Silveira, Thiago Lopes Trugillo
Brazil, Emilio Vital
Ponti, Moacir A.
Fernandes, Leandro A. F.
Avila, Sandra
e-Mail Addressgrodriguessganderla@gmail.com
Conference NameConference on Graphics, Patterns and Images, 34 (SIBGRAPI)
Conference LocationGramado, RS, Brazil (virtual)
Date18-22 Oct. 2021
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeUndergraduate Work
History (UTC)2021-09-13 10:35:02 :: grodriguessganderla@gmail.com -> administrator ::
2022-09-10 00:16:17 :: administrator -> :: 2021
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
KeywordsDrone
Detecção de Objetos
YOLOv5
AbstractThrough large data sets, it is possible to train and instruct a machine with skills to perform tasks previously performed only by humans. This possibility has become increasingly real with the use of Deep Learning and powerful algorithms that have been developed over time. Among them is YOLO, a Convolutional Neural Network algorithm that allows several uses, including the detection and classification of objects contained in images of urban environments, such as people and vehicles, allowing the identification and location of objects within the images. This work presents a model for detecting and classifying common object classes in urban environments - People, Small Vehicles, Medium-Vehicles and Large-Vehicles). For this project we used a combination of 3 datasets of aerial drone images of urban environments (Stanford Drone Dataset, Vision Meets Drone, The Unmanned Aerial Vehicle Benchmark Object Detection and Tracking). The result obtained from the initial training of this YOLO algorithm was an average accuracy of 67.2%.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2021 > Detecção e Classificação...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/45E3ET5
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/45E3ET5
Languagept
Target FileSIBGRAPI_2021_GUILHERME(1).pdf
User Groupgrodriguessganderla@gmail.com
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/45PQ3RS
Citing Item Listsid.inpe.br/sibgrapi/2021/11.12.11.46 4
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume


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