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- W2605456475 abstract "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 tower detecting problem where aggregate channel features are used to train the boost classifier. Adopting the sliding window paradigm, the electric tower can be located very fast. The main advantages of this approach are its efficiency and accuracy for processing huge quantity of image data. Obtaining highly encouraging results shows that it is really a promising technique." @default.
- W2605456475 created "2017-04-28" @default.
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- W2605456475 date "2017-04-12" @default.
- W2605456475 modified "2023-09-23" @default.
- W2605456475 title "Learning for Tower Detection of Power Line Inspection" @default.
- W2605456475 doi "https://doi.org/10.12783/dtcse/iccae2016/7194" @default.
- W2605456475 hasPublicationYear "2017" @default.
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