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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/45EAGM8
Repositorysid.inpe.br/sibgrapi/2021/09.15.01.13
Last Update2021:09.15.01.13.51 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2021/09.15.01.13.52
Metadata Last Update2022:09.10.00.16.17 (UTC) administrator
Citation KeyDomênicoLauRibRieJún:2021:EsCoRe
TitleUm Estudo Comparativo de Redes Convolucionais Profundas para Detecção de Insetos em Imagens
FormatOn-line
Year2021
Access Date2024, Apr. 26
Number of Files1
Size333 KiB
2. Context
Author1 Domênico, Jéssica Regina Di
2 Lau, Douglas
3 Ribeiro, Daniel Delfini
4 Rieder, Rafael
5 Júnior, Telmo De Cesaro
Affiliation1 Instituto Federal de Educação Sul-rio-grandense (IFSul)
2 Embrapa Trigo
3 Instituto Federal de Educação Sul-rio-grandense (IFSul)
4 Universidade de Passo Fundo (UPF)
5 Instituto Federal de Educação Sul-rio-grandense (IFSul)
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 Addresstelmojunior@ifsul.edu.br
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)2021-09-15 01:13:52 :: telmojunior@ifsul.edu.br -> 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
Keywordsyolo mask rcnn cnn cnn aphids
AbstractThis work presents a comparative study between two deep convolutional network models in tasks of identification and counting of insects in digital images, considering aphids (Hemiptera: Aphididae) and parasitoids (Hymenoptera: Aphelinidae and Braconidae, Aphidiinae). In this case study, each image can contain hundreds of specimens, debris, overlaps, and other insects with similar morphology, making the detection process difficult. In this sense, we compared the results obtained by the InsectCV system, which was based on Mask R-CNN, in terms of training time, inference, and precision, with a new model, trained with the DarkNet network. Using grayscale images with smaller dimensions, processing via GPU, and a one-stage convolutional network, it is possible to reduce the computational cost and increase the precision in the object detection task. Based on the 580 images used to validate the proposed model, it was possible to obtain a mean Average Precision of 79.9\%.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2021 > Um Estudo Comparativo...
doc Directory Contentaccess
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/45EAGM8
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/45EAGM8
Languagept
Target Filepaper.pdf
User Grouptelmojunior@ifsul.edu.br
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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