Close
Metadata

Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPEW34M/45EAGM8
Repositorysid.inpe.br/sibgrapi/2021/09.15.01.13
Last Update2021:09.15.01.13.51 (UTC) telmojunior@ifsul.edu.br
Metadatasid.inpe.br/sibgrapi/2021/09.15.01.13.52
Metadata Last Update2021:09.15.01.13.52 (UTC) telmojunior@ifsul.edu.br
Citation KeyDomênicoLauRibRieJún:2021:EsCoRe
TitleUm Estudo Comparativo de Redes Convolucionais Profundas para Detecção de Insetos em Imagens
FormatOn-line
Year2021
Access Date2021, Sep. 24
Number of Files1
Size333 KiB
Context area
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 (Virtual), Brazil
DateOctober 18th to October 22nd, 2021
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeUndergraduate Work
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
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%.
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
agreement.html 14/09/2021 22:13 1.3 KiB 
Conditions of access and use area
data URLhttp://urlib.net/rep/8JMKD3MGPEW34M/45EAGM8
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/45EAGM8
Languagept
Target Filepaper.pdf
User Grouptelmojunior@ifsul.edu.br
Visibilityshown
Allied materials area
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
Notes area
Empty Fieldsaccessionnumber archivingpolicy archivist area callnumber copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition nexthigherunit notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume
Description control area
e-Mail (login)telmojunior@ifsul.edu.br
update 

Close