| Full text | |
| Author(s): |
Jodas, Danilo Samuel
;
Brazolin, Sergio
;
Yojo, Takashi
;
de Lima, Reinaldo Araujo
;
Velasco, Giuliana Del Nero
;
Machado, Aline Ribeiro
;
Papa, Joao Paulo
;
IEEE Comp Soc
Total Authors: 8
|
| Document type: | Journal article |
| Source: | 2021 34TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI 2021); v. N/A, p. 8-pg., 2021-01-01. |
| 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. (AU) | |
| FAPESP's process: | 17/50343-2 - Institutional development plan in the area of digital transformation: advanced manufacturing and smart and sustainable cities (PDIp) |
| Grantee: | Zehbour Panossian |
| Support Opportunities: | Research Grants - State Research Institutes Modernization Program |
| FAPESP's process: | 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction? |
| Grantee: | Alexandre Xavier Falcão |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 19/18287-0 - Real-time Urban Forest Management Using Machine Learning |
| Grantee: | Danilo Samuel Jodas |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| FAPESP's process: | 19/07665-4 - Center for Artificial Intelligence |
| Grantee: | Fabio Gagliardi Cozman |
| Support Opportunities: | Research Grants - Research Program in eScience and Data Science - Research Centers in Engineering Program |