Surface Defect Detection using Novel Histogram Distance-based Multiple Template Anomalies Detection Algorithm

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    Surface defects in manufacturing are top challenges in various manufacturing field including LED manufacturing, die manufacturing and printing industry. Quality control through automated surface defect detection has been an emphasis to speed up the production without jeopardizing the quality of the product. However, complexity and flexibility in product design, specification and dataset availability posted challenges in existing referential-based algorithm. Golden template-based algorithms are sensitive to misalignment and product variations. Deep learning and its variant can be used as non-linear filter to segment anomalies area. However, deep learning requires huge labelled database and consume long learning time. Similarly, maximum likelihood-based algorithms require large database for learning. This research proposes a novel histogram distance based multiple templates anomalies detection (MTAD) algorithm to segment surface defect. Histogram distance based on kernel-wise histograms stacked across illumination normalized database of similar size can describe the degree of anomaly intuitively across the image. Then, surface defect can be justified intuitively according to anomaly heat map generated. The algorithm is tested against industrial samples and it can handle texture and design variation existed in the product while catching anomaly in real time. This research suggests future studies on extending dimensionality of the histogram. Suggested algorithm has wide range of application other than surface defect detection. For examples, video motion detection, decolorization detection on industrial lighting.

     

     


  • Keywords


    Histogram distance; Multiple templates; Referential method; Surface Defect Detection

  • References


      [1] D.-M. Tsai, M.-C. Chen, W.-C. Li, and W.-Y. Chiu, “A fast regularity measure for surface defect detection,” Mach. Vis. Appl., vol. 23, no. 5, pp. 869–886, Sep. 2012.

      [2] X. Xie, “A review of recent advances in surface defect detection using texture analysis techniques,” … Lett. Comput. Vis. Image Anal., vol. 7, no. 3, pp. 1–22, 2008.

      [3] D. M. Tsai and J. Y. Luo, “Mean shift-based defect detection in multicrystalline solar wafer surfaces,” IEEE Trans. Ind. Informatics, vol. 7, no. 1, pp. 125–135, 2011.

      [4] S. Huang and Y.-C. Pan, “Automated visual inspection in the semiconductor industry: A survey,” Comput. Ind., vol. 66, pp. 1–10, 2015.

      [5] M. Moganti, F. Ercal, C. H. Dagli, and S. Tsunekawa, “Automatic PCB Inspection Algorithms: A Survey,” Comput. Vis. Image Underst., vol. 63, no. 2, pp. 287–313, 1996.

      [6] S. Son, “Reference-based Defect Detection on OLED Images using Local Gaussian Distribution,” vol. 3, no. 3, pp. 19–34, 2015.

      [7] S. Mei, H. Yang, and Z. Yin, “An Unsupervised-Learning-Based Approach for Automated Defect Inspection on Textured Surfaces,” pp. 1–12, 2018.

      [8] P. Xie and S. U. Guan, “Golden-template self-generating method for patterned wafer inspection,” Mach. Vis. Appl., vol. 12, no. 3, pp. 149–156, 2000.

      [9] Z. Ibrahim and S. A. R. Al-attas, “Wavelet-Based Printed Circuit Board Inspection System,” Int. J. Signal Process., vol. 1, no. 2, pp. 73–79, 2005.

      [10] D. M. Tsai, C. T. Lin, and J. F. Chen, “The evaluation of normalized cross correlations for defect detection,” Pattern Recognit. Lett., vol. 24, no. 15, pp. 2525–2535, 2003.

      [11] D. M. Tsai and C. H. Yang, “A quantile-quantile plot based pattern matching for defect detection,” Pattern Recognit. Lett., vol. 26, no. 13, pp. 1948–1962, 2005.

      [12] D. M. Tsai, I. Y. Chiang, and Y. H. Tsai, “A shift-tolerant dissimilarity measure for surface defect detection,” IEEE Trans. Ind. Informatics, vol. 8, no. 1, pp. 128–137, 2012.

      [13] V. H. Gaidhane, Y. V Hote, and V. Singh, “An efficient similarity measure approach for PCB surface defect detection,” Pattern Anal. Appl., 2017.

      [14] H. Kong, J. Yang, and Z. Chen, “Accurate and Efficient Inspection of Speckle and Scratch Defects on Surfaces of Planar Products,” IEEE Trans. Ind. Informatics, vol. 13, no. 4, pp. 1855–1865, 2017.

      [15] R. Ren, T. Hung, and K. C. Tan, “A Generic Deep-Learning-Based Approach for Automated Surface Inspection,” IEEE Trans. Cybern., vol. 48, no. 3, pp. 929–940, 2018.

      [16] K. Briechle and U. D. Hanebeck, “Template matching using fast normalized cross correlation,” Proc. SPIE, vol. 4387, pp. 95–102, 2001.


 

View

Download

Article ID: 27693
 
DOI: 10.14419/ijet.v7i4.14.27693




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.