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Reference TypeConference Paper (Conference Proceedings)
Last Update2021: (UTC)
Metadata Last Update2021: (UTC)
Citation KeyDomênicoLauRibRieJún:2021:EsCoRe
TitleUm Estudo Comparativo de Redes Convolucionais Profundas para Detecção de Insetos em Imagens
Access Date2021, Sep. 24
Number of Files1
Size333 KiB
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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
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
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Is the master or a copy?is the master
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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%.
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