第18卷第4期 2010年4月
文章编号
1004-924X(2010)04-0981-07
光学精密工程
Optics and Precision Engineering
软性电路板金面缺陷的无监督检测
王庆香1.2李迪"，张舞杰"，叶峰1
（1.华南理工大学机械与汽车工程学院，广东广州510641； 2.广州中医药大学信息技术学院，广东广州510006）
Vol.18No.4
Apr.2010
摘要：为实现软性电路板(FPC)金面缺陷的准确自动检测，提出了一种以Gabor滤波器和MeanShift聚类算法为基础的完全无监督FPC金面缺陷检测方法。首先，用Gabor滤波器组、数学形态学与Gaussian平滑处理抽取待检测图像的多维特征；然后，使用主元分析(PCA)将每个像索特征维数降为二维；最后，使用MeanShift方法对二维特征数据进行聚类并将聚类的结果转化为二值图像。整个检测过程无需预先知道缺陷的类型和FPC金面的纹理类型，是一种完全无监督的检测方法。对带有各种缺陷的FPC金面进行检测实验，结果表明，该方法能够准确地将各类缺陷区域从背景区域中
分离出来，具有自动缺陷检测系统所要求的识别能力强、稳定性高的特点。关键词：缺陪降检测;Gabor滤波器；MeanShift聚类
中图分类号：TP274.3;TN407
文献标识码：A
Unsupervised defect detection for
goldsurfaceofflexibleprintedboard WANG Qing-xiang2,LI Di',ZHANGWu-jie',YEFeng
(1.School of Mechanical and AutomotiveEngineering,South China University of Technology,Guangzhou510641,China;2.School of Information Technology,
GuangzhouUniversity of ChineseMedicine,Guangzhou510006,China)
Abstract: A completely unsupervised defect detection method is proposed based on the Gabor filters and Mean Shift clustering to achieve the accurate automatic defect detection of a FPC gold surface. Firstly,the multi-dimension characteristics of an image to be detected are extracted by a series of pro cessing steps including Gabor filter banks, morphological open and Gaussian smoothing. Then, the Principal Component Analysis (PCA) is used to reduce the pixel characteristics from multi-dimension to 2-D for reducing computation time in the next clustering. Finally, Mean Shift method is applied to cluster pixels with 2-D characteristics and the results can be divided into defect and non-defect groups to produce the binary image. The whole process needs to neither predefine the type of defects nor the texture type of FPC gold surface, which can be defined as a completely unsupervised method of detec ting defects. A number of images of FPC gold surfaces with a variety of defects have been tested. De-tection results show that the proposed method can accurately separate all types of defect regions from
收稿日期：2009-06-11：修订日期：2009-09-07.
基金项目：广东省科技攻关计划资助项目（No.2008B01040004）