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Due to the influence of strong electric field, mechanical stress, pollution, temperature and humidity, insulators often appear anomalies such as internal cracks, surface damage, insulation impedance reduction and pollution flashover, which may cause serious an accident of power cut, so automatic detection of insulator defects is of great significance to ensure the safe operation of power network. According to the distance invariance between two adjacent umbrellas in an insulator, this paper presents an algorithm of detecting the position of lost umbrellas. Firstly,an image of insulator regions is extracted using an adaptive histogram-based segmentation method(ACHS), and its horizontal tilt is corrected. Secondly, the periodic parameters of the geometric structure of insulators are estimated by gray normalized correlation matching method. Finally, gray normalized correlation matching method is also used to detect the position of missing pieces of insulators. This proposed method is tested on 809 data set for unmanned aerial vehicle inspection, the accuracy and recall of the insulator drop detection are 95.8% and 91.9%, respectively. Compared with the existing methods, the advantage of this method is that there is no need to use large samples for statistical learning in advance and it has strong adaptability to the changes of scale, rotation, illumination, background and the variety of insulators.