浏览全部资源
扫码关注微信
1. 中国科学院 光电技术研究所,四川 成都,610209
2. 电子科技大学 光电信息学院,四川 成都,610054
收稿日期:2013-12-15,
修回日期:2014-01-25,
纸质出版日期:2014-07-25
移动端阅览
张静, 叶玉堂, 谢煜等. 金属圆柱工件缺陷的光电检测[J]. 光学精密工程, 2014,22(7): 1871-1876
ZHANG Jing, YE Yu-tang, XIE Yu etc. Optoelectronic inspection of defects for metal cylindrical workpieces[J]. Editorial Office of Optics and Precision Engineering, 2014,22(7): 1871-1876
张静, 叶玉堂, 谢煜等. 金属圆柱工件缺陷的光电检测[J]. 光学精密工程, 2014,22(7): 1871-1876 DOI: 10.3788/OPE.20142207.1871.
ZHANG Jing, YE Yu-tang, XIE Yu etc. Optoelectronic inspection of defects for metal cylindrical workpieces[J]. Editorial Office of Optics and Precision Engineering, 2014,22(7): 1871-1876 DOI: 10.3788/OPE.20142207.1871.
针对金属工件外观缺陷检测存在光学照明不均、检测缺陷种类繁多、检测系统识别率不高等问题,研究了检测金属圆柱工件缺陷的方法。分析了局部二元模式(LBP)与局部图像方差强度(LVAR)的基本原理,研究了两者在金属纹理表面缺陷检测中的具体实现方法。采用LBP反应局部图形空间纹理模式,LVAR突出图像强度对比信息,然后用LVAR计算结果作为权重值来调整LBP的局部纹理提取和度量结果,实现了金属圆柱工件的自动缺陷检测。实验中采用步进电机控制工件旋转,配合线阵相机采集圆柱工件的展开图像。实验结果显示,这种方法有效克服了金属材质光照不均的缺点,对大量缺陷种类具有较高的鲁棒性,其检出率高达95.1%,漏检率为0%,满足了工业检测要求。
To overcome the shortcomings of metal workpiece defect detection in optical uneven illumination
higher detection defect ranges and lower detection system recognition rate
a defect detection method was proposed.The basic principles of Local Binary Pattern (LBP) and Local Image Variance (LVAR) were analyzed
and their specific methods in the algorithm of metal cylindrical detection were discussed.The LBP was used to reflect local graphics texture pattern and the LVAR to outstand the contrast of image intensity.Then
the weight values calculated from LVAR were used to adjust the extraction and measurement of LBP local texture.Thus
the automatic detection of metal cylindrical workpieces was achieved.In the experiments
the rotation of workpieces was controlled by a stepper and the expanded images of cylindrical workpieces were captured by a linear CCD.The experimental results demonstrate that this method effectively overcome the shortcomings of metal uneven illumination and has high robustness to a large number of defect types.The detection rate has reached to 99.5% and missing rate to 0%
which meets industrial inspection requirements.
ZHENG H,KONG L X,NAHAVANDI S. Automatic inspection of metallic surface defects using genetic algorithms[J]. Journal of Materials Processing Technology,2002,125-126:427-433.
PERNEKOPF F,PAUL O L. Image acquisition techniques for automatic visual inspection of metallic surface[J]. NDT&E International,2003,36(8):609-617.
LEE S,CHANG L M,SKIBNIEWSKI M. Automated recognition of surface defects using digital color image processing[J]. Automation in Construction,2006,15(4):540-449.
黄杰贤,李迪,叶峰,等. 挠性印制电路板焊盘表面缺陷的检测[J]. 光学精密工程,2010,18(11):2443-2553. HUANG J X,LI D,YE F,et al. Detection of surface defection of solder on flexible printed circuit[J]. Opt. Precision Eng.,2010,18(11):2443-2553. (in Chinese)
FERNANDEZ C,PLATERO C,CAMPOY P, et al. . Vision system for online surface inspection in aluminium casting process. IEEE Conference on Industrial Electronics,Control,Instrumentation and Automation,1993:1854-1859.
吴平川,路同浚,王炎. 机器视觉与钢板表面缺陷的无损检测[J]. 无损检测,2000,22(1):13-16. WU P CH,LU T J,WANG Y. Machine vision technology and non-destructive detection of the surface defects in strip steel[J]. Non-Destructive Detection,2000,22(1):13-16. (in Chinese)
PIETIKAINEN M,HADID A,ZHAO G,et al. Computer Vision Using Local Binary Patterns[M]. Germany:Springer,2011:13-47.
FELZENSZWALB P,HUTTENLOCHER. Efficient graph-based image segmentation[J]. Int. Computer Vision,2004,59(2):167-181.
TAN X,TRIGGS B. Enhanced local texture feature sets for face recognition under difficult lighting conditions[J]. IEEE Transactions on Image Processing,2010,19(6):1635-1650.
JING ZH,YUTANG Y,YU X,et al. High density print circuit board line width measurement algorithm based on statistical process control theory[J]. Optik,2013,9(1):62-66.
NAKIB A,OULHADJ H,SIARRY P. Image histogram thresholding based on multiobjective optimization[J]. Signal Processing,2007,87(Ⅱ):2516-2534.
LORIS N,ALESSANDRA L,SHERYL B. Survey on LBP based texture descriptors for image classification[J]. Expert System with Applications,2012,39(3):3934-3641.
0
浏览量
172
下载量
12
CSCD
关联资源
相关文章
相关作者
相关机构