| 引用本文: | 张学武,吕艳云,丁燕琼,梁瑞宇.小波统计法的表面缺陷检测方法[J].控制理论与应用,2010,27(10):1331~1336.[点击复制] |
| ZHANG Xue-wu,LV Yan-yun,DING Yan-qiong,LIANG Rui-yu.Surface defect inspection based on wavelet statistical analysis[J].Control Theory and Technology,2010,27(10):1331~1336.[点击复制] |
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| 小波统计法的表面缺陷检测方法 |
| Surface defect inspection based on wavelet statistical analysis |
| 摘要点击 2216 全文点击 2348 投稿时间：2009-05-07 修订日期：2010-01-18 |
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| DOI编号 10.7641/j.issn.1000-8152.2010.10.CCTA090568 |
| 2010,27(10):1331-1336 |
| 中文关键词 缺陷检测 小波统计 强反射金属 机器视觉 |
| 英文关键词 defect inspection wavelet-based statistic strongly reflected metal machine vision |
| 基金项目 国家自然科学基金资助项目(60872096); 河海大学青年科技创新基金资助项目(XZX/07/3002.3); 常州市科技创新基金资助项目(CN2008033). |
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| 中文摘要 |
| 针对铜带表面缺陷的特点, 基于小波统计方法设计了对铜带表面缺陷检测的系统. 首先把铜带表面图像分为互不重叠的子图像, 再把子图像分为多个小波处理单元, 对每个小波处理单元进行db4紧支集正交小波一级分解, 在此基础上进行Hotelling T^2统计检测缺陷. 最后利用支持向量机进行缺陷分类. 实验中将基于小波的统计方法和基于灰度的差影法进行比较, 结果证明本文提出的方法识别率高, 特别对于用一般算法识别率较低的“起皮”缺陷达到96.7%的识别率. |
| 英文摘要 |
| According to the characteristics of defect image on copper strips surface, we design a surface defect detection system on the basis of wavelet-based multivariate statistical approach. First, the surface image is divided into sub-images; each sub-image is further segmented into multiple wavelet processing units. Then, each wavelet processing unit is decomposed by 1-D db4 wavelet function. The multivariate statistics of Hotelling T2 is then applied to detect the defects, and Support-Vector-Machines(SVM) is used as the defect classifier. The defect detection performances of the proposed approach are compared with those of the grayscale- difference method. Experimental results show that the proposed method has higher performances on identification; the recognition rate for the ripple defects achieves 96.7% which is unattainable
by common algorithms. |