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浙江大学学报(工学版)
J4  2010, Vol. 44 Issue (3): 448-452    DOI: 10.3785/j.issn.1008973X.2010.03.006
自动化技术、计算机技术     
基于多元图像分析的表面缺陷检测算法
 王宣银, 梁冬泰
浙江大学 流体传动及控制国家重点实验室,浙江 杭州 31007
Surface defect detection based on multivariate image analysis
 WANG Xuan-Yin, LIANG Dong-Tai
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摘要：

为了提高工业产品质量控制中表面缺陷检测的准确性和可靠性,提出一种基于多元图像分析（MIA)的表面缺陷检测算法.采用合格样本图像和被测图像堆叠构成多元被测图像,利用基于主成分分析(PCA)的多元图像处理方法,获得多元被测图像的主分量表示,将去掉第一主分量和噪音后的Q统计图像作为缺陷特征的检测空间,利用阈值处理检测缺陷.金属罐内壁的表面缺陷检测实验结果表明,与现有基于直方图阈值处理等检测方法相比,受照明不均匀影响小,提高了图像检测系统的准确性和鲁棒性,使缺陷的误检率大大降低.
Abstract:

 A new approach based on multivariate image analysis (MIA) was proposed to improve the accuracy and reliability of surface defect detection for industrial product quality control. Multivariate test images are constructed by stacking both the standard defectfree images and the test image. MIA technique uses multiway principal component analysis (PCA) to obtain the principal component scores of the multivariate test images. As the feature space for defect detection, the Qstatistic image is derived from the residuals which are left after the extraction of the first principal component and the noise. In the Qstatistic image, surface defects can be efficiently detected by using an appropriate threshold. Experimental results showed the effectiveness of the proposed method. Compared with the gray histogram based method, the proposed method has less sensitivity to the inhomogeneous of illumination, and has more robustness and reliability of defect detection with lower pseudo reject rate.
出版日期: 2012-03-20
:  TP391.41  
基金资助:

教育部新世纪优秀人才支持计划资助项目（NCET-04-0545）
通讯作者: 王宣银(1966-)，男，湖南衡阳人，教授，博导，主要从事机器人、智能机器与图像信息技术等研究工作.     E-mail: xywang@zju.edu.cn
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引用本文:

王宣银, 梁冬泰. 基于多元图像分析的表面缺陷检测算法[J]. J4, 2010, 44(3): 448-452.

WANG Xuan-Yin, LIANG Dong-Tai. Surface defect detection based on multivariate image analysis. J4, 2010, 44(3): 448-452.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008973X.2010.03.006    或    http://www.zjujournals.com/eng/CN/Y2010/V44/I3/448

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