Robust Instruments Position Estimation using Improved Kernelized Correlation Filter for Substation Patrol Robots

Abstract

Substation patrol robots (SIR) play an increasingly important role in ensuring the safe operation of substations. The robust and precise position estimating of the instruments to be inspected on the images are a prerequisite for accurately detecting the target states or obtaining the target readings under all-weather environment. In order to achieve high location accuracy of instrument, this study proposed an improved kernelized correlation filter (KCF) algorithm for achieving robust instrument location on images for SPR. Firstly, multiple templates are selected for training KCF classifier parameters. Then, reliable SURF matching-point determination method is designed, and the regions including reliable matching points are selected as the candidate regions, so that the searching range is narrowed. Finally, for KCF response surface of each candidate region, Single-Peak Constraint (SPC) is designed for locating target and reducing mismatching rate. Furthermore, experiments are performed for validating the effectiveness of the proposed algorithm, in which four instruments mainly including lightning arrester monitor and transformer thermometer are selected. The experimental results show that the proposed method has higher accuracy of target localization than traditional SURF-based position estimating method.