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基于3D塔架配准的绝缘子自爆缺陷检测
引用本文:翟荔婷,张冰怡,冯志勇,徐超.基于3D塔架配准的绝缘子自爆缺陷检测[J].计算机工程与科学,2016,38(8):1688-1694.
作者姓名:翟荔婷  张冰怡  冯志勇  徐超
作者单位:;1.天津大学计算机科学与技术学院;2.天津大学软件学院
摘    要:在航拍图像的绝缘子检测中,图像的拍摄条件会导致背景的复杂程度不同,如何保持在多样背景下的绝缘子检测效果成为急需解决的一个重要问题。提出一种自适应的绝缘子自爆缺陷检测方法,该方法利用3D模型定位,并将绝缘子自身的绝缘子片作为模板检测缺陷。首先利用每种塔架的3D模型图与其对应的标准图进行SIFT点匹配,再利用随机抽样一致性方法找到标准图到待检测图的转换矩阵,从而找到塔架上每个绝缘子的位置;然后将图像转为HSV模型后,利用小波变换处理H通道,再进行二值化;最后通过Radon变换确定二值化图像中绝缘子中轴的大致方向并转正,去掉噪声点,取第一片绝缘子片作为模板在中轴滑动,记录每片绝缘子位置的像素分布直方图,利用地球移动距离EMD方法判断分布是否存在异常,最后再显示出自爆绝缘子片的位置。测试结果表明,该方法的自爆缺陷检测效果良好,具有较高的正确率和鲁棒性。

关 键 词:3D模型  图像匹配  随机抽样一致性  HSV模型  滑动模板
收稿时间:2015-06-15
修稿时间:2016-08-25

Insulator blew detection based on match of 3D tower
ZHAI Li-ting,ZHANG Bing-yi,FENG Zhi-yong,XU Chao.Insulator blew detection based on match of 3D tower[J].Computer Engineering & Science,2016,38(8):1688-1694.
Authors:ZHAI Li-ting  ZHANG Bing-yi  FENG Zhi-yong  XU Chao
Affiliation:(1.School of Computer Science and Technology,Tianjin University,Tianjin 300350; 2.School of Computer Software,Tianjin University,Tianjin 300350,China)
Abstract:The conditions under which pictures are taken incur varied background complexity in aerial images. How to maintain consistent detection results in diverse backgrounds becomes an important issue. In this paper we propose an adaptive method for insulator blew detection. It locates the insulator with a 3D model and detects defects using the template of the insulator sheet. Firstly, we match the 3D model with standard image by the sift feature and find the transformation matrix from the standard image to the image to be tested via the RANSAC algorithm so as to locate all the insulators. Secondly, we transform the image from RGB to HSV, deal the H-channel with wavelet transform, and make it a binary image. We determine the direction of the axis in the binary image and turn it vertical by the Radon transform. After noise removed, we take the first insulator sheet as a model, which slides over the axis to record the histogram of every position. We employ the Earth Mover’s Distance (EMD) method to judge whether the histogram is normal or not, and locate the self-destruction position. Experimental results indicate the proposal is highly accurate and robust.
Keywords:3D model  image matching  RANSC  HSV model  sliding model  
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