基于谱残差视觉显著性的带钢表面缺陷检测

陈海永; 徐森; 刘坤; 孙鹤旭

doi:10.3788/OPE.20162410.2572

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基于谱残差视觉显著性的带钢表面缺陷检测

信息科学 | 更新时间：2020-07-07

- 基于谱残差视觉显著性的带钢表面缺陷检测
- Surface defect detection of steel strip based on spectral residual visual saliency
- 光学精密工程 2016年24卷第10期页码：2572-2580
- 作者机构：
  
  1.河北工业大学控制科学与工程学院, 天津 300130
  2.河北科技大学, 河北石家庄 050000
- 作者简介：
  
  陈海永(1980-),男,河南开封人,博士,副教授,2002年、2005年于哈尔滨理工大学分别获得学士、硕士学位,2008年于中国科学院获得博士学位,主要从事机器人视觉,机器人控制等工作。E-mail:haiyong.chen@hebut.edu.cn
  [ "刘坤(1980-),女,黑龙江哈尔滨人,博士,副教授,2001年于东北农业大学获得学士学位,2003年于哈尔滨工业大学获得硕士学位,2009年于清华大学获得博士学位。主要从事机器视觉与智能控制等工作。E-mail:Liukun03@mails.thu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金资助项目(61403119;61203275);河北省自然科学基金资助项目(F2014202071);天津市特派员科技计划资助项目(15JCTPJC55500)
- DOI：10.3788/OPE.20162410.2572
  中图分类号： TP391.4;TG142.15
- 收稿日期：2016-06-13，
  
  录用日期：2016-8-14，
  
  纸质出版日期：2016-10
- 稿件说明：
移动端阅览
陈海永, 徐森, 刘坤, 等. 基于谱残差视觉显著性的带钢表面缺陷检测[J]. 光学精密工程, 2016,24(10):2572-2580.

Hai-Yong CHEN, Sen XU, Kun LIU, et al. Surface defect detection of steel strip based on spectral residual visual saliency[J]. Optics and precision engineering, 2016, 24(10): 2572-2580.
陈海永, 徐森, 刘坤, 等. 基于谱残差视觉显著性的带钢表面缺陷检测[J]. 光学精密工程, 2016,24(10):2572-2580. DOI： 10.3788/OPE.20162410.2572.

Hai-Yong CHEN, Sen XU, Kun LIU, et al. Surface defect detection of steel strip based on spectral residual visual saliency[J]. Optics and precision engineering, 2016, 24(10): 2572-2580. DOI： 10.3788/OPE.20162410.2572.

摘要

针对带钢表面缺陷检测实时性要求高，采集的图像易受光照环境影响且缺陷特征弱等因素影响，提出一种基于谱残差视觉注意模型的带钢表面缺陷在线检测算法。首先，提出改进同态滤波方法对图像预处理，去除光照不均匀的影响，改善后续的分割结果。然后，构建谱残差视觉注意模型，通过对数频谱曲线差分得到缺陷显著图像。最后，提出加权马氏距离方法对显著图像阈值化增强，并利用连通区域标记法，标记出原带钢图像的缺陷位置。对提出的算法进行了实验验证，结果显示：该算法检测速度快，单幅图像平均检测耗时仅37.6 ms，满足带钢在线实时检测要求。在同一缺陷数据库与灰度投影法，多尺度Gabor边缘检测法和隐马尔可夫树模型法进行了性能对比，结果表明：本文算法对带钢常见8类缺陷类型，平均检测率达到了95.3%，且漏检率和误检率较低，有效性高于对比算法。

Abstract

As captured images for surface defect detection of a steel strip is vulnerable to lighting conditions

weaker defect characteristics and other factors

this paper proposes a new algorithm based on spectral residual visual attention mode to complete the strip surface defect detection in real time. Firstly

the homomorphic filtering method was proposed to preprocess the image to remove the influence of uneven illumination and to perfect the subsequent segmentation results. Then

a visual-attention model was constructed to obtain the defect saliency map by applying the subtraction to the logarithmic spectrum curve. Finally

the weighted Mahalanobis distance method was proposed to significantly enhance the saliency image thresholding. These locations of the defects in the original strip defect images were marked by using the connected-component labeling method. The proposed algorithm was verified by experiments. Experimental results show that the algorithm has a fast detection speed

and takes only 37.6 ms in the single image detection

meeting the requirements of the real-time detection. The comparative experiment with the gray projection method

multi-scale Gabor edge detection method and Markortree model was carried out in the same defect database

and the results show that average detection rate of the proposed algorithm reaches to 95.3% for 8 common types of defects. In the meantime

the missing rate and false positive rate are still low. These results validate the effectiveness of the algorithm.

关键词

Keywords

references

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