NDLI: A supervised approach to electric tower detection and classification for power line inspection

Content Provider	IEEE Xplore Digital Library
Author	Sampedro, C. Martinez, C. Chauhan, A. Campoy, P.
Copyright Year	2014
Description	Author affiliation: CAR UPM-CSIC, Univ. Politec. de Madrid, Madrid, Spain (Sampedro, C.; Martinez, C.; Chauhan, A.; Campoy, P.)
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 (Histograms of Oriented Gradients) features are used to train two MLP (multi-layer perceptron) neural networks. The first classifier is used for background-foreground segmentation, and the second multi-class MLP is used for classifying within 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, highly encouraging results were obtained. This shows that a learning-based approach is a promising technique for power line inspection.
Starting Page	1970
Ending Page	1977
File Size	1935323
Page Count	8
File Format	PDF
ISSN	21614407
e-ISBN	9781479914845
DOI	10.1109/IJCNN.2014.6889836
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Poles and towers Inspection Neural networks Image segmentation Training Histograms Feature extraction
Content Type	Text
Resource Type	Article

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