• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2015, Vol. 51 ›› Issue (6): 98-103.doi: 10.3901/JME.2015.06.098

• 仪器科学与技术 • 上一篇    下一篇


周鹏 徐科 刘顺华   

  1. 北京科技大学高效轧制国家工程研究中心 北京 100083
  • 出版日期:2015-03-20 发布日期:2015-03-20
  • 基金资助:

Surface Defect Recognition for Metals Based on Feature Fusion of Shearlets and Wavelets

ZHOU Peng XU Ke LIU Shunhua   

  1. National Engineering Research Center for Advanced Rolling Technology, University of Science and Technology Beijing, Beijing 100083
  • Online:2015-03-20 Published:2015-03-20

摘要: 金属表面缺陷具有复杂性和多样性。小波变换能很好地捕捉点奇异性信号,对点缺陷识别效果好;剪切波变换作为一种新的多尺度几何分析方法,对奇异曲线具有最优逼近性能和方向敏感性,因此对方向性的缺陷识别效果好。提出一种基于剪切波和小波特征融合的金属表面缺陷识别方法。对金属表面图像分别进行离散剪切波变换和小波变换,计算各尺度方向子带的平均值和方差值,并组成一高维特征矢量。用核保局投影算法对高维特征矢量进行降维,以去除特征间的冗余,得到低维特征矢量,并输入到支持向量机进行缺陷分类。以高温铸坯、中厚板、精密铝带三种典型金属为例,对从现场采集到的三种典型金属的样本库进行试验,结果表明:高温铸坯、中厚板、精密铝带缺陷样本的识别率分别达到93.95%、98.27%、92.5%。试验结果证明了该方法可有效应用于不同类型金属的表面缺陷识别,具有通用性。

关键词: 表面缺陷, 剪切波变换, 特征提取, 小波变换

Abstract: Surface defects of metals are often with complexity and diversity. Wavelet transform is effective to detect singularities, it is applicable for recognition of point defects. As a relatively new method of multiscale geometric analysis(MGA), shearlet transform has good performance of directivity and optimal approximation, and it is applicable for recognition of defects with determined directions. A new recognition method of surface defects for metals is proposed, and it is based on feature fusion of shearlet transform and wavelet transform. Images of metals are decomposed into multiple directional sub-bands with shearlet transform and wavelet transform respectively. Then, means and variances of all sub-bands are computed and combined as a high dimensional feature vector. kernel locality preserving projection(KLPP) is applied to the high dimensional feature vector to remove redundant information between features. A low dimensional feature vector is generated and input to support vector machine(SVM) for classification of defects. Tested with samples captured from production lines of three typical metals, including high temperature slabs, medium and heavy plates and aluminium strips. Results show that classification rates of testing sets for high temperature slabs, medium and heavy plates and aluminium strips are 93.95%, 98.27% and 92.5% respectively. It is verified that the proposed method is a general algorithm of defect recognition for different types of metals.

Key words: feature extraction, shearlet transform, surface defect, wavelet transform