Observatorio de I+D+i UPM

Memorias de investigación
Communications at congresses:
A Supervised Approach to Electric Tower Detection and Classification for Power Line Inspection.
Year:2014
Research Areas
  • Artificial intelligence,
  • Engineering,
  • Computer vision
Information
Abstract
Inspection of power line infrastructures must be periodically conducted by electric companies in order to ensure reliable electric power distribution. Research efforts are focused on automating the power line inspection process by looking for strategies that satisfy the different requirements of the inspection: simultaneously detect transmission towers, check for defects, and analyze security distances. Following this direction, this paper proposes a supervised learning approach for solving the tower detection and classification problem, where HOG features are used to train two MLP (multi-layer perceptron) neural networks. The first classifier is used for background- foreground separation, and the second multi-class MLP is used for classifying 4 different types of electric towers. A thorough evaluation of the tower detection and classification approach has been carried out on image data from real inspections tasks with different types of towers and backgrounds. In the different evaluations that were conducted highly encouraging results were obtained. This shows that a learning-based approach is a promising technique for power line inspection.
International
Si
Congress
IEEE World Congress on Computational Intelligence
960
Place
Beijing, China
Reviewers
Si
ISBN/ISSN
10.1109/IJCNN.2014.6889836
Start Date
06/07/2014
End Date
11/07/2014
From page
To page
Participants
  • Autor: Carlos Sampedro Perez (UPM)
  • Autor: Carol Viviana Martinez Luna (UPM)
  • Autor: Aneesh Chauhan . (UPM)
  • Autor: Pascual Campoy Cervera (UPM)
Research Group, Departaments and Institutes related
  • Creador: Centro o Instituto I+D+i: Centro de Automática y Robótica (CAR). Centro Mixto UPM-CSIC
  • Departamento: Automática, Ingeniería Eléctrica y Electrónica e Informática Industrial
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