Detection method of power line under uneven lightness for flying-walking power line inspection robot

Abstract

Due to the variation of sunlight conditions resulting in uneven lightness in images, the details of target objects tend to hide in the dark or bright regions, which is adverse to following image processing. To reliably land on the power line under the change of lightness, the flying-walking power line inspection robot (FPLIR) needs reliable detection for the power line. In this paper, a machine vision-based detection method of power line is proposed to adapt different lightness. Firstly, a visual system of the FPLIR is designed to collect and process power line images. Secondly, the multi-scale retinex (MSR) algorithm is used to reduce the influence of lightness. Then, the local binary pattern (LBP) map of power line image is generated by the LBP operator and is divided into many blocks. An LBP histogram vector is calculated for every block, then the first-order entropy and second-order entropy of every histogram vector are calculated. Finally, the first-order entropy, the second-order entropy, and the edge density of power line image are used as the feature vector of fuzzy c-means (FCM) to obtain the power line region. The experimental result shows that the accuracy of the proposed method is 82.6%, which is 9.3% more than the method without image enhancement. Thus, the proposed method can effectively detect power line, improving the robustness and accuracy of power line detection (PLD) during the FPLIR landing.