Recognition of natural ice types on in-service glass insulators based on texture feature descriptor

Abstract

For in-service transmission lines, insulator icing condition depends on analysis of images collected by cameras installed on towers, and ice type analysis is mostly completed by human beings, which is infeasible to monitor a large area. Although image processing is successfully applied to segmentation of insulators from aerial images, there are few literatures on automatically determining ice types on insulators via image processing. In this paper, a recognition method of ice types on in-service glass insulators is established based on a texture feature description operator. Uniform Local Binary Patterns (ULBP) and Improved Uniform Local Binary Patterns (IULBP) were adopted for texture feature extraction of typical images of six ice types including glaze ice, heavy rime, medium rime, light rime, partial rime and snow. The six ice types are concluded based on icing insulator images and corresponding meteorological data from China Southern Power Grid Transmission Line (Icing) Online Monitoring System. Ice types are recognized by correlation coefficient calculation of texture histograms. The experiment results show that IULBP obtains better results for recognition of the six ice types as each ice type has distinguished texture features.