计算机科学 ›› 2016, Vol. 43 ›› Issue (Z6): 222-225.doi: 10.11896/j.issn.1002-137X.2016.6A.054

• 模式识别与图像处理 • 上一篇    下一篇

航拍图像中绝缘子缺陷的检测与定位

方挺,韩家明   

  1. 安徽工业大学电气与信息工程学院 马鞍山243000,安徽工业大学电气与信息工程学院 马鞍山243000
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受国家自然科学基金资助

Detection and Localization of Insulator Defects in Aerial Images

FANG Ting and HAN Jia-ming   

  • Online:2018-12-01 Published:2018-12-01

摘要: 长期暴露在自然环境下的绝缘子易产生自爆缺失故障,对此提出了一种绝缘子缺陷检测和定位的图像处理方法。依据绝缘子在航拍图像中的型态特征,采用最大类间方差法以及中值滤波进行图像预处理,提出基于粒子群优化参数的蚁群算法来检测其中的绝缘子轮廓及其数目,最终实现在原图中标记出缺陷绝缘子所在位置的目的。该方法针对简单背景下绝缘子缺陷的标记具有良好的效果,并为进一步实现复杂背景下绝缘子缺陷检测和定位提供了预研基础。

关键词: 最大类间方差法,粒子群算法,蚁群算法,绝缘子缺陷检测和定位

Abstract: The insulator is easy to result in self-shattering fault if exposed in the natural environment for long time.For this situation,this paper presented an image processing method for insulator defect detection and location.Based on the pattern characteristics of insulator in aerial image,the between-cluster variance method and median filtering were used to preprocess the image.The ant colony algorithm with optimized parameters obtained by particle swarm optimization algorithm was proposed to detect the contour and number of insulator in the aerial image.At last,the location of defective insulator was marked in the original image.This method has good effect for defective insulator detection under simple background,and establishes pre-research foundation to realize insulator defect detection and localization under complex background.

Key words: Between-cluster variance method,Particle swarm optimization algorithm,Ant colony algorithm,Insulator defect detection and location

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