计算机与现代化

• 应用与开发 • 上一篇    下一篇

小波分解在带钢缺陷检测中的应用

  

  1. (西安工程大学机电工程学院,陕西 西安 710048)
  • 收稿日期:2014-05-19 出版日期:2014-07-16 发布日期:2014-07-17
  • 作者简介:徐帅华(1990-),男,河南周口人,西安工程大学机电工程学院硕士研究生,研究方向:图像处理与模式识别; 管声启(1971-),男,安徽安庆人,副教授,博士,研究方向:图像处理,智能信号检测与模式识别。
  • 基金资助:
    陕西省教育厅科研计划项目(2013JK1083); 西安工程大学博士科研启动基金资助项目(BS1005)

Application of Wavelet Decomposition in Steel Strip Defect Detection

  1. (College of Mechanical & Electrical Engineering, Xi’an Polytechnic University, Xi’an 710048, China)
  • Received:2014-05-19 Online:2014-07-16 Published:2014-07-17

摘要: 光照不均会降低带钢图像的质量,在研究带钢缺陷特点的基础上,提出一种新的带钢缺陷检测方法。首先,对图像取对数处理并进行小波分解,其次分别对小波分解的子图进行同态滤波,然后对滤波后的子图进行中央周边差操作形成差分子图,在此基础上,对差分子图进行融合处理并取指数处理得到高对比度的缺陷图像,最后采用Otsu分割方法对缺陷图像分割。实验结果表明,该方法能增强缺陷图像对比度,图像细节部分清晰,同时可抑制噪声的影响,能够有效地实现缺陷图像的分割。

关键词: 小波分解, 图像分割, 缺陷检测

Abstract: The influence of illumination reduces the quality of strip image, a new kind of steel strip detection method is put forward through analysis of strip defect characteristics. First of all, the logarithm of strip image gray value is decomposed into a series of sub-graph through the wavelet transform. Secondly, every sub-graph of wavelet decomposition is treated by homomorphism, and then the center-surround difference operation is used to construct difference sub-map. On this basis, difference sub-maps are selected for image fusion and exponential operation is used to get the high contrast defect image. Finally, steel strip defect is detected through the segmentation method of maximum between-cluster variance. The result of experiments shows that this method can enhance the defect image contrast with clear image details, inhibit the effect of noise, and effectively realize the rapid detection of strip defect image.

Key words: wavelet decomposition, image segmentation, defect detection

中图分类号: