Browse > Article

An Inspection System for Multilayer Co-Extrusion Blown Plastic Film Line  

Hahn, Jong Woo (Korea University of Technology and Education, School of Computer Science and Engineering)
Mahmood, Muhammad Tariq (Korea University of Technology and Education, School of Computer Science and Engineering)
Choi, Young Kyu (Korea University of Technology and Education, School of Computer Science and Engineering)
Publication Information
Journal of the Semiconductor & Display Technology / v.11, no.2, 2012 , pp. 45-51 More about this Journal
Multilayer co-extrusion blown film construction is a popular technique for producing plastic films for various packaging industries. Automated detection of defective films can improve the quality of film production process. In this paper, we propose a film inspection system that can detect and classify film defects robustly. In our system, first, film images are acquired through a high speed line-scan camera under an appropriate lighting system. In order to detect and classify film defects, an inspection algorithm is developed. The algorithm divides the typical film defects into two groups: intensity-based and texture-based. Intensity-based defects are classified based on geometric features. Whereas, to classify texture-based defects, a texture analysis technique based on local binary pattern (LBP) is adopted. Experimental results revealed that our film inspection system is effective in detecting and classifying defects for the multilayer co-extrusion blown film construction line.
Multilayer Co-extrusion Blown Plastic Film; Inspection System; Film Defects; Defect Detection and Classification; Local Binary Pattern;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 M. Heikkila and M. Pietikainen, "A Texture-Based Method for Modeling the Background and Detecting Moving Objects," IEEE Trans. on PAMI, Vol. 28, No. 4, pp. 657-662, April 2006.
2 A. Rosenfeld, and A. Kak, "Digital Picture Processing", Academic Press, New York, 1982.
3 R. Gonzalez, and R. Woods, "Digital Image Processing", Prentice-Hall Inc., New Jersey, 2002.
4 R. Duda, and P. Hart, "Use of Hough transformation to detect lines and curves in pictures", Communications of the ACM, Vol 15, No. 1, pp. 11-15, 1972.   DOI
5 R. Haralick and L. Shapiro, "Computer and Robot Vision," vol 1&2, Addison-Wesley, 1993
6 "Matrox Imaging Library, User's Guide," Matrox Inc.
7 C. Kuo, C. Chiu, and K. Peng, "Application of wavelet transformation in optical thin film defect automatic inspection system", Proceedings of the 2011 International Conference on Wavelet Analysis and Pattern Recognition, Guilin, 10-13 July, 2011.
8 T. Josip , T. Petković , J. Krapac , S. Lončarić and M. Sercer, "Automated Visual Inspection of Plastic Products," Proceedings of ERK 2002.
9 H. Lin, "Automated visual inspection of ripple defects using wavelet characteristic based multivariate statistical approach", Image and Vision Computing, Vol. 25, No. 11, pp. 1785-1801, 2007.   DOI   ScienceOn
10 C. J. Kuo, and C. Tsai, "Automatic recognition of fabric nature by using the approach of texture analysis", Textile Research Journal, Vol 76, No. 5, pp. 375-382, 2006.   DOI   ScienceOn
11 A. Kumar and G. Pang, "Defect detection in textured materials using Gabor filters," IEEE Transactions on Industry Applications, vol. 38, no. 2, pp. 425-440, 2002.   DOI   ScienceOn
12 한종우, 최영규, "제지공정의 실시간 결함 검출을 위한 영상 기반 웹검사 시스템," 반도체 및 디스플레이장비학회지 제 9 권 제 2 호, pp. 1-7, 2010.