Transmission equipment image recognition based on Ensemble Learning

Abstract

Unmanned aerial vehicle (UAV) inspection is the most popular inspection method in power transmission line inspection. UAV inspection mainly obtains the inspection image of power transmission equipment through machine vision and detects it. Image classification technology is mainly used for the identification of power transmission equipment, which can be used for fault diagnosis after sorting out the equipment. In order to accurately classify transmission equipment images, a classification method based on ensemble learning is proposed. This paper classifies and identifies four kinds of equipment in transmission line, including insulator, damper, interval rods, and corona ring. The experimental results show that the efficiency of the proposed ensemble learning method is better than that of single machine learning classifier.