| Zhang Xuewu(张学武),Xu Lizhong,Ding Yanqiong,Fan Xinnan.[J].高技术通讯(英文),2012,18(1):26~32 |
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| Automated visual inspection of surface defects based on compound moment invariants and support vector machine① |
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| DOI： |
| 中文关键词: |
| 英文关键词: copper strips surface (CSS) defects, compound invariant moments, support vector machine (SVM), visual inspection system, neural network |
| 基金项目: |
| Author Name | Affiliation | | Zhang Xuewu(张学武) | | | Xu Lizhong | | | Ding Yanqiong | | | Fan Xinnan | |
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| 中文摘要: |
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| 英文摘要: |
| The traditional inspection methods are mostly based on manual inspection which is very likely to make erroneous judgments due to personal subjectivity or eye fatigue, and can’t satisfy the accuracy. To overcome these difficulties, we develop a machine vision inspection system. We first compare several kinds of methods for feature extraction and classification, and then present a real-time automated visual inspection system for copper strips surface (CSS) defects based on compound moment invariants and support vector machine (SVM). The proposed method first processes images collected by hardware system, and then extracts feature characteristics based on grayscale characteristics and morphologic characteristics (Hu and Zernike compound moment invariants). Finally, we use SVM to classify the CSS defects. Furthermore, performance comparisons among SVM, back propagation (BP) and radial basis function (RBF) neural networks have been involved. Experimental results show that the proposed approach achieves an accuracy of 95.8% in detecting CSS defects. |
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