INSULATOR CLASSIFICATION USING SCALE INVARIANT FEATURE TRANSFORM WITH K-NEAREST NEIGHBOUR
Authors: *Iruansi U, Oyebode K.O
DOI Info: N/A
The demand of uninterrupted supply of electricity is equally increasing the need to monitor the electric power grid. Insulators are part of the components that make up the electric power grid. Hence, faulty insulators may affect the mechanical and electrical performance of an electric power grid, which can lead to the flow of leakage currents through the line supports. The traditional method of inspection is inadequate in meeting the growth and development of the present electric power grid. Hence an automated system such as the computer vision method is presently being explored as a means to resolve this crisis safely, speedily and accurately. This paper presents a method of insulator classification using scale invariant feature transform and k-nearest neighbor. The case study validates the efficacy of the proposed methodology for insulator classification.
Affiliations: *Department of Computer Engineering, Faculty of Engineering, University of Benin, PMB 1154, Benin City, Nigeria
Keywords: Active Contour, Classification, Insulator, K-nearest Neighbour, Scale Invariant Feature Transform
Published date: 2018/06/30