Surface defect detection of steel strip based on spectral residual visual saliency

Hai-Yong CHEN; Sen XU; Kun LIU; He-Xu SUN

doi:10.3788/OPE.20162410.2572

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Surface defect detection of steel strip based on spectral residual visual saliency

Information Sciences | 更新时间：2020-07-07

- Surface defect detection of steel strip based on spectral residual visual saliency
- Optics and Precision Engineering Vol. 24, Issue 10, Pages: 2572-2580(2016)
- 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：
- 基金信息：
- DOI：10.3788/OPE.20162410.2572
  CLC： TP391.4;TG142.15
- Received：13 June 2016，
  
  Accepted：14 August 2016，
  
  Published：2016-10
- 稿件说明：
移动端阅览
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：

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

NEOGI N,MOHANTAL D K, DUTTA P K. Review of vision based steel surface inspection systems[J].EURASIP Journal on Image and Video Processing, 2014(1):1-19.

乔凯,陈健,李中国,等. 锥束CT图像中的印刷电路板导线自动检测方法[J]. 光学精密工程,2016,24(2):413-421.

QIAO K,CHEN J,LI ZH G,et al..Automatic printed circuit board wire detecting method of cone beam CT image[J].Opt.Precision Eng.,2016,24(2):413-421.(in Chinese)

GONG R F, CHU M X, WANG A N, et al.. A fast detection method for region of defect on strip steel surface[J]. Isij International, 2015, 55(1):207-212.

徐科,杨朝霖,周鹏. 热轧带钢表面缺陷在线检测的方法与工业应用[J]. 机械工程学报,2009,45(4):111-114+124.

XU K,YANG CH L,ZHOU P.Technology of on-line surface inspection for Hot-rolled steel strips and its industrial application[J].Journal of Mechanical Engineering,2009,45(4):111-114+124.(in Chinese)

王宏,朱德生,唐威. 一种基于灰度投影的带钢表面缺陷检测算法[J]. 东北大学学报(自然科学版),2008,29(3):375-377.

WANG H,ZHU D SH,TANG W,An algorithm of strip surface defect detection based on gray scale projection[J].Journal of Northeastern University(Natural Science),2008,29(3):375-377.(in Chinese)

赵久梁,颜云辉,刘伟嵬,等. 板带钢表面缺陷检测系统的多尺度边缘检测算法[J]. 东北大学学报(自然科学版),2010,31(3):432-435.

ZHAO J L,YAN Y H,LIU W W,et al..A multi-scale edge detection method of steel strip surface defects online detection system[J].Journal of Northeastern University(Natural Science),2010,31(3):432-435.(in Chinese)

徐科,宋敏,杨朝霖,等. 隐马尔可夫树模型在带钢表面缺陷在线检测中的应用[J]. 机械工程学报,2013,49(22):34-40.

XU K,SONG M,YANG CH L,et al..Application of hidden Markov tree model to on-line detection of surface defects for steel strips[J].Journal of Mechanical Engineering,2013,49(22):34-40.(in Chinese)

GHORAI S,MUKHERJEE A, GANGADARAN M, et al.. Automatic defect detection on hot-rolled flat steel products[J]. IEEE Transactions on Instrumentation and Measurement, 2013, 62(3):612-621.

彭铁根,何永辉,李兵虎,等. 基于TDI成像技术的镀锡带钢表面质量在线检测系统研发[J]. 红外与激光工程,2014,43(1):294-299.

PENG T G,HE Y H,LI B H,et al.. Research and development of tin steel strip surface online inspection system based on TDI imaging technology[J].Infrared and Laser Engineering,2014,43(1):294-299.(in chinese)

赵宏伟,陈霄,刘萍萍,等. 视觉显著目标的自适应分割[J]. 光学精密工程,2013,21(2):531-538.

ZHAO H W,CHEN X,LIU P P,et al..Adaptive segmentation for visual salient object[J].Opt. Precision Eng.,2013,21(2):531-538.(in Chinese)

ZHAO Q, KOCH C.Learning saliency-based visual attention:A review[J]. Signal Processing, 2013, 93(6):1401-1407.

GOFERMAN S, ZELNIK-MANOR L, TAL A. Context-aware saliency detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(10):1915-1926.

丛家慧,颜云辉. 视觉注意机制在带钢表面缺陷检测中的应用[J]. 中国机械工程,2011,22(10):1189-1192+1221.

CONG J H,YAN Y H.Application of human visual attention mechanism in surface defect inspection of steel strip[J]. Journal of Mechanical Engineering,2011,22(10):1189-1192+1221.(in Chinese)

XU S H, GUAN S Q, CHEN L L.Steel strip defect detection based on human visual attention mechanism model[C].Applied Mechanics and Materials, 2014, 530:456-462.

杨永敏,樊继壮,赵杰. 强反射表面缺陷图像预处理[J]. 光学精密工程,2010,20(10):2288-2296.

YANG Y M,FAN J ZH,ZHAO J.Preprocessing for highly reflective surface defect image[J].Opt.Precision Eng.,2010,20(10):2288-2296.(in Chinese)

LIU W, HE P, LI H, et al.. Improvement on the algorithm of Homomorphic filtering[J]. Advances in Biomedical Engineering, 2012, 11:120.

HOU X, ZHANG L. Saliency detection:A spectral residual approach[C].IEEE Conference on Computer Vision and Pattern Recognition,2007,CVPR'07,2007:1-8.

CUI X, LIU Q, METAXAS D. Temporal spectral residual:fast motion saliency detection[C]. Proceedings of the 17th ACM International Conference on Multimedia, ACM, 2009:617-620.

SMITH E C, LEWICKIL M S. Efficient auditory coding[J]. Nature, 2006, 439(7079):978-982.

孙照蕾,惠斌,秦莫凡,等. 红外图像显著目标检测算法[J]. 红外与激光工程,2015,44(9):2633-2637.

SUN ZH L,HUI B,QIN M F,et al..Object detection method based on saliency measure for infrared radiation image[J]. Infrared and Laser Engineering,2015,44(9):2633-2637.(in Chinese)

DE MAESSCHALCK R,JOUAN-RIMBAUD D,MASSART D L. The mahalanobis distance[J]. Chemometrics and Intelligent Laboratory Systems, 2000, 50(1):1-18.

ZHAO X, LI Y, ZHAO Q. Mahalanobis distance based on fuzzy clustering algorithm for image segmentation[J]. Digital Signal Processing, 2015, 43:8-16.

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