%0 Journal Article
%T Surface defect inspection based on wavelet statistical analysis<br>小波统计法的表面缺陷检测方法
%A ZHANG Xue-wu
%A LV Yan-yun
%A DING Yan-qiong
%A LIANG Rui-yu
%A <br>张学武
%A 吕艳云
%A 丁燕琼
%A 梁瑞宇
%J 控制理论与应用
%D 2010
%I 
%X According to the characteristics of defect image on copper strips surface, we design a surface defect detection system on the basis of wavelet-based multivariate statistical approach. First, the surface image is divided into sub-images; each sub-image is further segmented into multiple wavelet processing units. Then, each wavelet processing unit is decomposed by 1-D db4 wavelet function. The multivariate statistics of Hotelling T2 is then applied to detect the defects, and Support-Vector-Machines(SVM) is used as the defect classifier. The defect detection performances of the proposed approach are compared with those of the grayscale- difference method. Experimental results show that the proposed method has higher performances on identification; the recognition rate for the ripple defects achieves 96.7% which is unattainable by common algorithms.
%K defect inspection
%K wavelet-based statistic
%K strongly reflected metal
%K machine vision<br>缺陷检测
%K 小波统计
%K 强反射金属
%K 机器视觉
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=970898A57DFC021F93AB51667BAED7F7&aid=979F7DB26218C3480A8B8010B925CEAC&yid=140ECF96957D60B2&vid=DB817633AA4F79B9&iid=F3090AE9B60B7ED1&sid=09002DF587B7129E&eid=F4C7ED9FD9D7FC69&journal_id=1000-8152&journal_name=控制理论与应用&referenced_num=0&reference_num=0