Frontiers of Optoelectronics

, Volume 10, Issue 2, pp 151–159 | Cite as

Defect detection on button surfaces with the weighted least-squares model

  • Yu Han
  • Yubin Wu
  • Danhua Cao
  • Peng Yun
Research Article


Defect detection is important in quality assurance on production lines. This paper presents a fast machine-vision-based surface defect detection method using the weighted least-squares model. We assume that an inspection image can be regarded as a combination of a defect-free template image and a residual image. The defect-free template image is generated from training samples adaptively, and the residual image is the result of the subtraction between each inspection image and corresponding defect-free template image. In the weighted least-squares model, the residual error near the edge is suppressed to reduce the false alarms caused by spatial misalignment. Experiment results on different types of buttons show that the proposed method is robust to illumination vibration and rotation deviation and produces results that are better than those of two other methods.


machine vision surface defect detection weighted least-squares model 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Li W B, Lu C H, Zhang J C. A lower envelope Weber contrast detection algorithm for steel bar surface pit defects. Optics & Laser Technology, 2013, 45(1): 654–659CrossRefGoogle Scholar
  2. 2.
    Crispin A J, Rankov V. Automated inspection of PCB components using a genetic algorithm template-matching approach. International Journal of Advanced Manufacturing Technology, 2007, 35(3): 293–300CrossRefGoogle Scholar
  3. 3.
    Arivazhagan S, Ganesan L, Bama S. Fault segmentation in fabric images using Gabor wavelet transform. Machine Vision and Applications, 2006, 16(6): 356–363CrossRefGoogle Scholar
  4. 4.
    Li W C, Tsai D M. Wavelet-based defect detection in solar wafer images with inhomogeneous texture. Pattern Recognition, 2012, 45(2): 742–756Google Scholar
  5. 5.
    Tsai D M, Wu S C, Chiu W Y. Defect detection in solar modules using ICA basis images. IEEE Transactions on Industrial Informatics, 2013, 9(1): 122–131CrossRefGoogle Scholar
  6. 6.
    Cen Y G, Zhao R Z, Cen L H, Cui L H, Miao Z J, Wei Z. Defect inspection for TFT-LCD images based on the low-rank matrix reconstruction. Neurocomputing, 2015, 149: 1206–1215CrossRefGoogle Scholar
  7. 7.
    Zhou W, Fei M, Zhou H, Li K. A sparse representation based fast detection method for surface defect detection of bottle caps. Neurocomputing, 2014, 123: 406–414CrossRefGoogle Scholar
  8. 8.
    Bai X, Fang Y, Lin W, Wang L, Ju B F. Saliency-based defect detection in industrial images by using phase spectrum. IEEE Transactions on Industrial Informatics, 2014, 10(4): 2135–2145CrossRefGoogle Scholar
  9. 9.
    Tsai D M, Chiang I Y, Tsai Y H. A shift-tolerant dissimilarity measure for surface defect detection. IEEE Transactions on Industrial Informatics, 2012, 8(1): 128–137CrossRefGoogle Scholar
  10. 10.
    Chan C H, Pang G K. Fabric defect detection by Fourier analysis. IEEE Transactions on Industry Applications, 2000, 36(5): 1267–1276CrossRefGoogle Scholar
  11. 11.
    Ngan H Y T, Pang G K H, Yung S P, Ng M K. Wavelet based methods on patterned fabric defect detection. Pattern Recognition, 2005, 38(4): 559–576CrossRefGoogle Scholar
  12. 12.
    Yang X, Pang G, Yung N. Robust fabric defect detection and classification using multiple adaptive wavelets. IEE Proceedings–Vision Image and Signal Processing, 2005, 152(6): 715CrossRefGoogle Scholar
  13. 13.
    Ralló M, Millán M S, Escofet J. Unsupervised novelty detection using Gabor filters for defect segmentation in textures. Journal of the Optical Society of America A, Optics, Image Science, and Vision, 2009, 26(9): 1967–1976CrossRefGoogle Scholar
  14. 14.
    Kumar A, Pang G K. Defect detection in textured materials using Gabor filters. IEEE Transactions on Industry Applications, 2002, 38(2): 425–440CrossRefGoogle Scholar
  15. 15.
    Wang C C, Jiang B C, Lin J Y, Chu C C. Machine vision-based defect detection in IC images using the partial information correlation coefficient. IEEE Transactions on Semiconductor Manufacturing, 2013, 26(3): 378–384CrossRefGoogle Scholar
  16. 16.
    Zontak M, Cohen I. Defect detection in patterned wafers using anisotropic kernels. Machine Vision and Applications, 2010, 21(2): 129–141CrossRefzbMATHGoogle Scholar
  17. 17.
    Stauffer C, Grimson W E L. Adaptive background mixture models for real-time tracking. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1999, 2: 246–252Google Scholar
  18. 18.
    Kaewtrakulpong P, Bowden R. An improved adaptive background mixture model for real-time tracking with shadow detection.Springer US, 2002: 135–144Google Scholar
  19. 19.
    Zivkovic Z. Improved adaptive Gaussian mixture model for background subtraction. In: Proceedings of International Conference on Pattern Recognition, 2004Google Scholar

Copyright information

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.School of Optical and Electronic InformationHuazhong University of Science and TechnologyWuhanChina

Personalised recommendations