基于主成分分析的表面缺陷自动检测算法

摘要

In order to detect the surface defects, this paper proposes an automatic detection algorithm based on Principal Component Analysis(PCA). The process uses PCA to make the image reconstruction to enhance the defects, and makes comparison with the original image and reconstruction image to get the defect information. The defects are detected after the statistical process control binarization. Experimental results show that the detection effect of this algorithm is good, operation speed is fast, for 80 different surface images, average defects detection rate is 80%.%为检测产品表面的缺陷,提出一种基于主成分分析的自动检测算法.利用主成分分析法进行图像重构,以增强缺陷特征,对比原图像与重构图像,得到缺陷信息,通过统计过程控制二值化方法检测出缺陷.实验结果表明,该算法检测效果较好,运算速度较快,对于80张不同的表面图片,平均缺陷检测率达80％.