Recognition of trees using Computer Vision to prevent interruptions in the Power Distribution System
Authors: 
Heuber Gustavo Frazao de Lima, Ronaldo Martins Costa, Anderson da Silva Soares, Gustavo Teodoro LaureanoAuthors Info & Claims
Abstract
The electrical distribution is a critical activity since many people depend of this service. Faults in the distribution system occur from several factors that can damage the system and therefore interrupt the supply of energy. Among the various factors that may cause problems this work proposes a automatic detection of trees near or even in the distribution network. In order to avoid that the trees to force or even rupture of the distribution cables, are made the pruning of the trees that have some kind of risk to the network. However, this activity is usually manual and teams must sift through all the network for problems. The main objective of this work is to propose a process, based on computer vision, which allows the automated identification of nearby trees or under the power distribution network from aerial images provided by Google Earth.
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Sponsors
- Fundacao de Amparo a Pesquisa do Estado de Goias: FAPEG
- SBC: Brazilian Computer Society
- Institute of Informatics/Federal University of Goias: INF/UFG
- Faculdades ALFA: Faculdades ALFA
- Global RH Solutions: Global RH Solutions
- Secretaria de Ciencia, Tecnologia e Inovacao do Estado de Goias: SECTEC-GO
- CAPES: Brazilian Higher Education Funding Council
In-Cooperation
- SIGAPP: ACM Special Interest Group on Applied Computing
- SIGMIS: ACM Special Interest Group on Management Information Systems
- AIS: Assoc for Information Systems
Publisher
Brazilian Computer Society
Porto Alegre, Brazil
Publication History
Published: 26 May 2015
Accepted: 01 April 2015
Received: 01 February 2015
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Author Tags
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- Short-paper
- Research
- Refereed limited
Conference
SBSI '15
Sponsor:
- Fundacao de Amparo a Pesquisa do Estado de Goias
- SBC
- Institute of Informatics/Federal University of Goias
- Faculdades ALFA
- Global RH Solutions
- Secretaria de Ciencia, Tecnologia e Inovacao do Estado de Goias
- CAPES
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SBSI '15 Paper Acceptance Rate 101 of 313 submissions, 32%;
Overall Acceptance Rate 181 of 557 submissions, 32%
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