基于CCD图像分析的空轨轨道梁表面缺陷检测  

Surface Defect Inspection of Sky Railway Track Beam Based on CCD Image Analysis

作　　者：魏德豪 刘孜学[1] 陈庆 王孔明[1] 康波[2] WEI Dehao;LIU Zixue;CHEN Qing;WANG Kongming;KANG Bo(China Railway Eryuan Engineering Group Co.,Ltd.,Chengdu 610031,China;School of Automation Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China)

机构地区：[1]中铁二院工程集团有限责任公司,成都610031 [2]电子科技大学自动化工程学院,成都611731

出　　处：《无损检测》2019年第9期65-69,共5页Nondestructive Testing

摘　　要：针对空轨轨道梁的表面缺陷,采用图像分析的方法进行检测.对于电荷耦合器件(CCD)摄像机采集到的钢板表面图像信息,先通过基于均值和方差的粗检方法,获取正常样本,即利用一定大小的滑动窗口计算表面图像的均值和方差,分析确定阈值后将样本分成疑似缺陷样本和正常样本.接着在粗检的基础上,采用基于积分图的Bayes细检方法提高准确度,即用压缩感知算法得到缺陷的特征样本,进一步将粗检过程中错分到缺陷样本中的正常样本剔除.试验结果表明,该方法对钢板表面缺陷的检出率达到98%以上,准确率达到95%以上.Aiming at the surface defect problem of sky railway track beam, the method of image analysis is adopted to inspect. Firstly, for the image information of steel plate surface which is collected by CCD camera, the normal samples are obtained by means of a rough inspection method based on mean and variance. The mean and variance of surface images are calculated by sliding windows of a certain size, and the samples are divided into suspected defect samples and normal samples after the threshold is determined. Then, on the basis of rough inspection, Bayes fine inspection method based on integral graph is adopted to improve the accuracy. Using the compressed sensing algorithm, the characteristic samples of defects are obtained, and the normal samples which are wrongly divided into defect samples in the rough inspection process can be removed. The experimental results show that the detection rate and accuracy of this method for surface defects of steel plate are greater than 98% and 95% respectively.

关 键 词：空轨 轨道梁 图像分析 表面缺陷检测 

分 类 号：U232[交通运输工程—道路与铁道工程] TG115.28[金属学及工艺—物理冶金]