@InProceedings{DomźnicoLauRibRieJśn:2021:EsCoRe,
               author = "Dom{\^e}nico, J{\'e}ssica Regina Di and Lau, Douglas and 
                         Ribeiro, Daniel Delfini and Rieder, Rafael and J{\'u}nior, Telmo 
                         De Cesaro",
          affiliation = "{Instituto Federal de Educa{\c{c}}{\~a}o Sul-rio-grandense 
                         (IFSul)} and {Embrapa Trigo} and {Instituto Federal de 
                         Educa{\c{c}}{\~a}o Sul-rio-grandense (IFSul)} and {Universidade 
                         de Passo Fundo (UPF)} and {Instituto Federal de 
                         Educa{\c{c}}{\~a}o Sul-rio-grandense (IFSul)}",
                title = "Um Estudo Comparativo de Redes Convolucionais Profundas para 
                         Detec{\c{c}}{\~a}o de Insetos em Imagens",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "yolo mask rcnn cnn cnn aphids.",
             abstract = "This 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\%.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
             language = "pt",
                  ibi = "8JMKD3MGPEW34M/45EAGM8",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45EAGM8",
           targetfile = "paper.pdf",
        urlaccessdate = "2024, May 06"
}