软性电路板金面缺陷的无监督检测

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Mean Shift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)将每个像素特征维数降为二维;最后,使用Mean Shift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。

Unsupervised defect detection for gold surface of flexible printed board

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 processing 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 detecting defects. A number of images of FPC gold surfaces with a variety of defects have been tested. Detection results show that the proposed method can accurately separate all types of defect regions from the background and has the characteristics of high stability and strong ability to identify defects.