| 1. Identity statement | ||||
| Reference Type | Conference Paper (Conference Proceedings) | |||
| Site | sibgrapi.sid.inpe.br | |||
| Holder Code | ibi 8JMKD3MGPEW34M/46T9EHH | |||
| Identifier | 8JMKD3MGPEW34M/45C9DCP | |||
| Repository | sid.inpe.br/sibgrapi/2021/09.02.11.50 | |||
| Last Update | 2021:09.02.11.50.38 (UTC) administrator | |||
| Metadata Repository | sid.inpe.br/sibgrapi/2021/09.02.11.50.38 | |||
| Metadata Last Update | 2022:06.14.00.00.20 (UTC) administrator | |||
| DOI | 10.1109/SIBGRAPI54419.2021.00057 | |||
| Citation Key | JodasBrYoLiVeMaPa:2021:DeLeAp | |||
| Title | A Deep Learning-based Approach for Tree Trunk Segmentation  | |||
| Format | On-line | |||
| Year | 2021 | |||
| Access Date | 2024, May 06 | |||
| Number of Files | 1 | |||
| Size | 1202 KiB | |||
| 2. Context | ||||
| Author | 1 Jodas, Danilo Samuel 2 Brazolin, Sergio 3 Yojo, Takashi 4 Lima, Reinaldo Araujo de 5 Velasco, Giuliana Del Nero 6 Machado, Aline Ribeiro 7 Papa, João Paulo | |||
| Affiliation | 1 Department of Computing, São Paulo State University, Brazil 2 Institute for Technological Research, University of São Paulo, Brazil 3 Institute for Technological Research, University of São Paulo, Brazil 4 Institute for Technological Research, University of São Paulo, Brazil 5 Institute for Technological Research, University of São Paulo, Brazil 6 Institute for Technological Research, University of São Paulo, Brazil 7 Department of Computing, São Paulo State University, Brazil | |||
| Editor | Paiva, 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 Address | danilojodas@gmail.com | |||
| Conference Name | Conference on Graphics, Patterns and Images, 34 (SIBGRAPI) | |||
| Conference Location | Gramado, RS, Brazil (virtual) | |||
| Date | 18-22 Oct. 2021 | |||
| Publisher | IEEE Computer Society | |||
| Publisher City | Los Alamitos | |||
| Book Title | Proceedings | |||
| Tertiary Type | Full Paper | |||
| History (UTC) | 2021-09-02 11:50:38 :: danilojodas@gmail.com -> administrator :: 2022-03-02 00:54:15 :: administrator -> menottid@gmail.com :: 2021 2022-03-02 13:26:45 :: menottid@gmail.com -> administrator :: 2021 2022-06-14 00:00:20 :: administrator -> :: 2021 | |||
| 3. Content and structure | ||||
| Is the master or a copy? | is the master | |||
| Content Stage | completed | |||
| Transferable | 1 | |||
| Version Type | finaldraft | |||
| 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. | |||
| Arrangement 1 | urlib.net > SDLA > Fonds > SIBGRAPI 2021 > A Deep Learning-based... | |||
| Arrangement 2 | urlib.net > A Deep Learning-based... | |||
| doc Directory Content | access | |||
| source Directory Content | there are no files | |||
| agreement Directory Content |
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| 4. Conditions of access and use | ||||
| data URL | http://urlib.net/ibi/8JMKD3MGPEW34M/45C9DCP | |||
| zipped data URL | http://urlib.net/zip/8JMKD3MGPEW34M/45C9DCP | |||
| Language | en | |||
| Target File | paper.pdf | |||
| User Group | danilojodas@gmail.com | |||
| Visibility | shown | |||
| Update Permission | not transferred | |||
| 5. Allied materials | ||||
| Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 | |||
| Next Higher Units | 8JMKD3MGPEW34M/45PQ3RS 8JMKD3MGPEW34M/4742MCS | |||
| Citing Item List | sid.inpe.br/sibgrapi/2021/11.12.11.46 3 | |||
| Host Collection | sid.inpe.br/banon/2001/03.30.15.38 | |||
| 6. Notes | ||||
| Empty Fields | archivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination 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 volume | |||