@InProceedings{JodasBrYoLiVeMaPa:2021:DeLeAp,
               author = "Jodas, Danilo Samuel and Brazolin, Sergio and Yojo, Takashi and 
                         Lima, Reinaldo Araujo de and Velasco, Giuliana Del Nero and 
                         Machado, Aline Ribeiro and Papa, Jo{\~a}o Paulo",
          affiliation = "Department of Computing, S{\~a}o Paulo State University, Brazil  
                         and Institute for Technological Research, University of S{\~a}o 
                         Paulo, Brazil  and Institute for Technological Research, 
                         University of S{\~a}o Paulo, Brazil  and Institute for 
                         Technological Research, University of S{\~a}o Paulo, Brazil  and 
                         Institute for Technological Research, University of S{\~a}o 
                         Paulo, Brazil  and Institute for Technological Research, 
                         University of S{\~a}o Paulo, Brazil  and Department of Computing, 
                         S{\~a}o Paulo State University, Brazil",
                title = "A Deep Learning-based Approach for Tree Trunk Segmentation",
            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 = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Deep learning, convolutional neural networks, image processing, 
                         semantic segmentation, urban forest.",
             abstract = "Recently, the real-time monitoring of the urban ecosystem has 
                         raised the attention of many municipal forestry management 
                         services. The proper maintenance of trees is seen as crucial to 
                         guarantee the quality and safety of the streetscape. However, the 
                         current analysis still involves the time-consuming fieldwork 
                         conducted for extracting the measurements of each part of the 
                         tree, including the angle and diameter of the trunk, to cite a 
                         few. Therefore, real-time monitoring is thoroughly necessary for 
                         the rapid identification of the constituent parts of the trees in 
                         images of the urban environment and the automatic estimation of 
                         their physical measures. This paper presents a method to segment 
                         the tree trunks in photographs of the municipal regions. To 
                         accomplish such a task, we introduce a semantic segmentation 
                         convolutional neural network architecture that incorporates a 
                         depthwise residual block to the well-known U-Net model to reduce 
                         the parameters required to create the network. Then, we perform a 
                         post-processing step to refine the segmented regions by removing 
                         the additional binary areas not related to the tree trunk. Lastly, 
                         the proposed method also extracts the central line of the 
                         identified region for future computation of the trunk 
                         measurements. Compared with the original U-Net architecture, the 
                         obtained results confirm the robustness of the proposed 
                         approaches, including similar evaluation metrics and the 
                         significant reduction of the network size.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
                  doi = "10.1109/SIBGRAPI54419.2021.00057",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI54419.2021.00057",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45C9DCP",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45C9DCP",
           targetfile = "paper.pdf",
        urlaccessdate = "2024, May 06"
}