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工程科学与技术:2012,44(3):147-152
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基于Curvelet变换和支持向量机的磁瓦表面缺陷识别方法
(四川大学 制造科学与工程学院)
Defect Detection on Magnetic Tile Surfaces Based on Fast Discrete Curvelet Transform and Support Vector Machine
(School of Manufacturing Sci. and Eng., Sichuan Univ.)
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投稿时间:2011-10-14    修订日期:2012-03-26
中文摘要: 针对磁瓦表面缺陷对比度低、自动识别困难的问题,作者提出了一种对磁瓦图像应用快速离散Curvelet变换(FDCT)提取特征,并用支持向量机(SVM)分类器进行分类的磁瓦微小缺陷自动识别方法。该方法首先对磁瓦图像做分块处理,并对各分块图像应用FDCT,计算分解系数的l^2范数,获得磁瓦不同方向的纹理频域特征;然后以归一化的分解系数l^2范数作为支持向量机分类器的特征向量,对图像做出分类。对不同缺陷占比的图像进行实验测试,结果显示,当缺陷部分占分块图像的比例在1/64以上时正确识别率大于83%。
Abstract:Difficulties exist in automatically inspecting surface defects because of the low intensity image contrast. To overcome these difficulties, a textures analysis method for detecting defects on the magnetic tile surfaces was described. In this methodology the original image was divided into several equal sized squares, and decomposed based on a fast discrete curvelet transform (FDCT) at different scales and orientations. Then thel^2norms on the curvelet coefficients were calculated as the feature vector for support vector machine (SVM) classifier. The experimental results showed that the defects retrieval accuracy achieved 83% when defects accounted for more than 1/64 of magnetic tile image.
文章编号:201100910     中图分类号:    文献标志码:
基金项目:国家科技支撑计划课题资助项目(2006BAF01A07);四川省高新技术产业重大关键技术资助项目(2010GZ0051)
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蒋红海,殷国富,刘培勇,尹湘云.基于Curvelet变换和支持向量机的磁瓦表面缺陷识别方法[J].工程科学与技术,2012,44(3):147-152.
Jiang Honghai,Yin Guofu,Liu Peiyong,Yin Xiangyun.Defect Detection on Magnetic Tile Surfaces Based on Fast Discrete Curvelet Transform and Support Vector Machine[J].Advanced Engineering Sciences,2012,44(3):147-152.