Self-Shattering Defect Detection of Glass Insulators Based on Spatial Features

Energies. 2019;12(3):543 DOI 10.3390/en12030543

 

Journal Homepage

Journal Title: Energies

ISSN: 1996-1073 (Print)

Publisher: MDPI AG

LCC Subject Category: Technology

Country of publisher: Switzerland

Language of fulltext: English

Full-text formats available: PDF, HTML

 

AUTHORS

Haiyan Cheng (Hebei Engineering Research Center of Simulation and Optimized Control for Power Generation, North China Electric Power University, Baoding 071003, China)
Yongjie Zhai (Hebei Engineering Research Center of Simulation and Optimized Control for Power Generation, North China Electric Power University, Baoding 071003, China)
Rui Chen (Hebei Engineering Research Center of Simulation and Optimized Control for Power Generation, North China Electric Power University, Baoding 071003, China)
Di Wang (Power China Guizhou Electric Power Design & Research Institute Co., LTD, Guiyang 550002, China)
Ze Dong (Hebei Engineering Research Center of Simulation and Optimized Control for Power Generation, North China Electric Power University, Baoding 071003, China)
Yutao Wang (Hebei Engineering Research Center of Simulation and Optimized Control for Power Generation, North China Electric Power University, Baoding 071003, China)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 11 weeks

 

Abstract | Full Text

During an automatic power transmission line inspection, a large number of images are collected by unmanned aerial vehicles (UAVs) to detect existing defects in transmission line components, especially insulators. However, with twin insulator strings in the inspection images, when the umbrella skirts of the rear string are obstructed by the front string, defect detection becomes difficult. To solve this problem, we propose a method to detect self-shattering defects of insulators based on spatial features contained in images. Firstly, the images are segmented according to the particular color features of glass insulators, and the main axes of insulator strings in the images are adjusted to the horizontal direction. Then, the connected regions of insulators in the images are marked. After that, the vertical lengths of the regions, the number of insulator pixels in the regions, as well as the horizontal distances between two adjacent connected regions are selected as spatial features, based on which defect discriminants are formulated. Finally, experiments are performed using the proposed formula to detect self-shattering defects in the insulators, using the spatial distribution of the connected regions to locate the defects. The experiment results indicate that the proposed method has good detection accuracy and localization precision.