A Deep Learning-based Approach for Tree Trunk Segmentation

Autor(es)

Jodas, Danilo Samuel ; Brazolin, Sergio ; Yojo, Takashi ; de Lima, Reinaldo Araujo ; Velasco, Giuliana Del Nero ; Machado, Aline Ribeiro ; Papa, Joao Paulo ; IEEE Comp Soc

• Tipo de documento: Artigo Científico
• Fonte: 2021 34TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI 2021); v. N/A, p. 8-pg., 2021-01-01.

Resumo

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)

• Processo FAPESP: 17/50343-2 - Plano de desenvolvimento institucional na área de transformação digital: manufatura avançada e cidades inteligentes e sustentáveis (PDIp)
• Beneficiário: Zehbour Panossian
• Modalidade de apoio: Auxílio à Pesquisa - Programa Modernização de Institutos Estaduais de Pesquisa
• Processo FAPESP: 14/12236-1 - AnImaLS: Anotação de Imagem em Larga Escala: o que máquinas e especialistas podem aprender interagindo?
• Beneficiário: Alexandre Xavier Falcão
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 19/18287-0 - Gestão de Florestas Urbanas em Tempo Real Utilizando Aprendizado de Máquina
• Beneficiário: Danilo Samuel Jodas
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 19/07665-4 - Centro de Inteligência Artificial
• Beneficiário: Fabio Gagliardi Cozman
• Modalidade de apoio: Auxílio à Pesquisa - Programa eScience e Data Science - Centros de Pesquisa em Engenharia