Automatic Recognition of Insulator from UAV Infrared Image Based on Periodic Textural Feature

被引：0

作者：

Peng X. ^{[1
]}

Liang F. ^{[2
,3
]}

Qian J. ^{[1
]}

Yang B. ^{[2
,3
]}

Chen C. ^{[2
,3
]}

Zheng X. ^{[1
]}

机构：

[1] Guangdong Electric Power Research Institute, Guangzhou

[2] State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan

[3] Engineering Research Center for Spatio-Temporal Data Smart Acquisition and Application, Ministry of Education of China, Wuhan University, Wuhan

来源：

Gaodianya Jishu/High Voltage Engineering | 2019年 / 45卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Automatic recognition; Clustering; Infrared image; Insulators; Periodic textural feature; Transmission line; UAV inspection;

D O I：

10.13336/j.1003-6520.hve.20190226033

中图分类号：

学科分类号：

摘要：

Infrared images have been widely used in fault detection of insulators. However, it is difficult to deal with large volumes of images collected by Unmanned Aerial Vehicle (UAV) just using the traditional manual diagnosis way. Consequently, we proposed a novel method to automatically recognize and detect insulators in infrared images collected by UAV. First, the image edges are extracted from infrared image using Laplace operator, then, a histogram of edge density along the lines crossing the image is constructed, periodic textual features are extracted, and the insulators are finally recognized by a two-step clustering method. Infrared images with different backgrounds (plant, farmland, tower) are selected as the test data, and the recognition rate by our method is over 85%. Experiments show that our method can automatically recognize insulators from the UAV infrared images with a complex background, and can be further applied for intelligent infrared diagnosis using UAV. © 2019, High Voltage Engineering Editorial Department of CEPRI. All right reserved.

引用

页码：922 / 928

页数：6

共 16 条

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