Signal and Information Processing

Detection of Small Surface Defects Based on Machine Vision

Expand
  • 1. School of Mechanical and Electrical Engineering, Soochow University, Suzhou 215021,Jiangsu Province, China
    2. Center of Robotics and Microsystem, Soochow University, Suzhou 215021, Jiangsu Province, China

Received date: 2011-11-09

  Revised date: 2012-02-20

  Online published: 2012-09-25

Abstract

This paper proposes an effective method to be used in fast detection of small surface defects. The space domain gradient method is useful to enhance the surface image. However, we show that the Otsu method cannot produce satisfied result in segmenting small defects in a large surface image. To detect small defects and improve the performance of Otsu method, an algorithm based on the distribution of variances of image blocks is developed to search of small defect regions. Analysis and experiments show that the proposed method can be applied for fast detection of small surface defects.

Cite this article

HE Zhi-yong1;2, SUN Li-ning1;2, RUI Yan-nian1 . Detection of Small Surface Defects Based on Machine Vision[J]. Journal of Applied Sciences, 2012 , 30(5) : 531 -537 . DOI: 10.3969/j.issn.0255-8297.2012.05.015

References

[1] Tsai D M, Luo Jieyu. Mean shift-based defect detection in multicrystalline solar wafer surfaces [J]. IEEE Transactions on Industrial Informatics, 2011, 7(1):125-135.
[2] Yeh C H, Wu F C, Ji W L, Huang C Y. A waveletbased approach in detecting visual defects on semiconductor wafer dies [J]. IEEE Transactions on Semiconductor Manufacturing, 2010, 23(2): 284-292.
[3] Xie Xianghua, Majid M. TEXEMS: texture exemplars for defect detection on random textured surfaces [J]. IEEE Transactions on Pattern Analysis and
Machine Intelligence, 2007, 29(8): 1454-1464.
[4] 王宣银,梁冬泰. 基于多元图像分析的表面缺陷检测算法[J]. 浙江大学学报:工学版,2010, 44(3): 448-452.
Wang Xuanyin, Liang Dongtai. Surface defect detection based on multivariate image analysis [J]. Journal of Zhejiang University: Engineering Science,
2010, 44(3): 448-452. (in Chinese)
[5] 沈会良,张宏刚,李志能. 基于图像配准的STNLCD外观缺陷检测[J] 光电工程,2008, 35(9): 60-65.
Shen Huiliang, Zhang Honggang, Li Zhineng. Detection of STN-LCD defects based on image registration[J]. Opto-electronic Engineering, 2008, 35(9):
60-65. (in Chinese)
[6] Otsu N. A threshold selection method from graylevel histogram [J]. IEEE Transactions on System Man and Cybernetic, 1979, 9(1): 62-66.
[7] Yeh C H,Wu F C. An image enhancement technique in inspecting visual defects of polarizers in TFT-LCD industry [C]//International Conference on Computer Modeling and Simulation, 2009: 257-261.
[8] Gonzalez R F, Woods R E, Digital image processing [M]. 2 ed. Prentice Hall, New Jersey, 2002: 134-137.
[9] Sezgin M, Sankur B. Survey over image thresholding techniques and quantitative performance evaluation [J]. Journal of Electronic Imaging, 2004, 13(1):146-165.
[10] Kittler J, Illingworth J. Minimum error thresholding [J]. Pattern Recognition, 1986, 19(1): 41-47.
[11] Kittler J, Illingworth J. On threshold selection using clustering criteria [J]. IEEE Transactions on Systems, Man, and Cybernetics, 1985, SMC-15(5):
652-655.
[12] 刘建庄,粟文青. 灰度图像的二维Otsu 自动阈值分割法[J]. 自动化学报,1993, 19(1): 101-105.
Liu Jianzhuang, Li Wenqing. Automatic thresholding of gray-level pictures using two-dimension Otsu method [J]. Acta Automatica Sinica, 1993, 19(1):
101-105. (in Chinese)
[13] 许向阳,宋恩民,金良海. Otsu 准则的阈值性质分析 [J]. 电子学报,2009, 37(12): 2716-2719.
Xu Xiangyang, Song Enmin, Jin Lianghai. Characteristic analysis of threshold based on Otsu criterion [J]. Chinese Journal of Electronics, 2009, 37(12):
2716-2719. (in Chinese)
[14] Lee S U, Chung S Y, Park R H. A comparative performance study of several global thresholding techniques for segmentation [J]. Computer Vision,
Graphics and Image Processing, 1990, 52(2):171-190.
[15] 王中宇,付继华,孟浩,杨文平. 基于灰色关联分析和区域生长的微小缺陷提取[J]. 农业机械学报,2008, 39(12): 166-169.
Wang Zhongyu, Fu Jihua, Meng Hao, Yang Wenping. Small defect extracting based on region growing algorithm and grey relational analysis [J]. Transactions of the Chinese Society for Agricultural Machinery,2008, 39(12): 166-169. (in Chinese)

Outlines

/