Learning for Tower Detection of Power Line Inspection

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.