| Issue |
MATEC Web Conf.
Volume 100, 2017
13th Global Congress on Manufacturing and Management (GCMM 2016)
|
|
|---|---|---|
| Article Number | 03010 | |
| Number of page(s) | 7 | |
| Section | Part 3: Manufacturing innovation and Advanced manufacturing technology | |
| DOI | https://doi.org/10.1051/matecconf/201710003010 | |
| Published online | 08 March 2017 | |
Detection for Power line Inspection
Hei Longjiang Electric Power Research Institute, Harbin, Hei Longjiang, China
Corresponding E-mail: han_bing@vip.sina.com
Power line inspection is very important for electric company to keep good maintenance of power line infrastructure and ensure reliable electric power distribution. Research efforts focus on automating the inspection process by looking for strategies to satisfy all kinds of requirements. Following this direction, this paper proposes a learning approach for all kinds of detecting problems, where aggregate channel features are used to train the boost classifier. Adopting the sliding window paradigm, the electric tower, insulator and nest can be located very fast. The main advantage of this approach is its efficiency and accuracy for processing huge quantity of image data. Obtaining highly encouraging results shows that it is really a promising technique.
Key words: Power line / inspection / aggregate channel features
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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