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Glass insulator recognition based on HSL color space and SIFT matching

Yongjie Zhai, Yang Wu, Haiyan Cheng, Zhenbing Zhao

This paper presents a method for recognizing glass insulators in aerial images from helicopter patrolling of power lines, in order to improve work efficiency, compared with manual inspection. This technique works by rough location and local recognition. In rough location, the hue and lightness components in HSL color space were extracted initially to segment with their relevance to glass characteristic, instead of traditional algorithm in RGB model. And insulators are roughly located by morphology, connected components analysis. Then, we select sub-modules from insulator samples, using hierarchical clustering based on SIFT matching rates and recognize insulators locally by matching method. Some experiments on aerial images indicate that our approach avoid requirements of mass high-quality samples and shows significantly improved performance on detection accuracy.