Journal of Intelligent Manufacturing

, Volume 11, Issue 5, pp 485–499 | Cite as

A neural network approach for defect identification and classification on leather fabric

  • Choonjong Kwak
  • Jose A. Ventura
  • Karim Tofang-Sazi


In this paper, an automated vision system is presented to detect and classify surface defects on leather fabric. Visual defects in a gray-level image are located through thresholding and morphological processing, and their geometric information is immediately reported. Three input feature sets are proposed and tested to find the best set to characterize five types of defects: lines, holes, stains, wears, and knots. Two multilayered perceptron models with one and two hidden layers are tested for the classification of defects. If multiple line defects are identified on a given image as a result of classification, a line combination test is conducted to check if they are parts of larger line defects. Experimental results on 140 defect samples show that two-layered perceptrons are better than three-layered perceptrons for this problem. The classification results of this neural network approach are compared with those of a decision tree approach. The comparison shows that the neural network classifier provides better classification accuracy despite longer training times.

Vision inspection defect classification neural network feature selection classifier selection 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Antognetti, P. and Milutinovic, V. (1991) Neural Networks: Concepts, Applications, and Implementations, Prentice Hall, Englewood Cliffs, NJ.Google Scholar
  2. Augusteijn, M. F., Clemens, L. E. and Shaw, K. A. (1995) Performance evaluation of texture measures for ground cover identification in satellite images by means of a neural network classifier. IEEE Transactions on Geoscience and Remote Sensing, 33(3), 616-626.Google Scholar
  3. Brown, D. E., Corruble, V. and Pittard, C. L. (1993) A comparison of decision tree classifiers with backpropagation neural networks for multimodal classification problems. Pattern Recognition, 26(6), 953-961.Google Scholar
  4. Brzakovic, D., Beck, H. and Sufi, N. (1990) An approach to defect detection in materials characterized by complex textures. Pattern Recognition, 23(1/2), 99-107.Google Scholar
  5. Chetverikov, D. (1987) Texture imperfection. Pattern Recognition Letters, 6, 45-50.Google Scholar
  6. Chou, P. B., Rao, A. R., Sturzenbecker, M. C., Wu, F. Y. and Brecher, V. H. (1997) Automatic defect classification for semiconductor manufacturing. Machine Vision and Applications, 9, 201-214.Google Scholar
  7. Cohen, F. S., Fan, Z. and Attali, S. (1991) Automated inspection of textile fabrics using textural models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(8), 803-808.Google Scholar
  8. Conners, R. W. and Harlow, C. A. (1980) Toward a structural textural analyzer based on statistical methods. Computer Graphics and Image Processing, 12(3), 224-256.Google Scholar
  9. Freeman, J. A. (1991) Neural Networks: Algorithms, Applications, and Programming Techniques, Addison-Wesley, Reading, MA.Google Scholar
  10. Gupta, L., Sayeh, M. R. and Tammana, R. (1990) A neural network approach to robust shape classification. Pattern Recognition, 23(6), 563-568.Google Scholar
  11. Hecht-Nielsen, R. (1990) Neurocomputing, Addison-Wesley, New York, NY.Google Scholar
  12. Horn, B. K. P. (1986) Robot Vision, McGraw-Hill, New York, NY.Google Scholar
  13. Jain, A. and Zongker, D. (1997) Feature selection: evaluation, application, and small sample performance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(2), 153-158.Google Scholar
  14. Kwak, C., Ventura, J. A. and Tofang-Sazi, K. (1998) Automated defect inspection and classification for leather fabric. Technical Report, Department of Industrial and Manufacturing Engineering. The Pennsylvania State University, University Park, PA.Google Scholar
  15. Lisboa, P. J. G. (1992) Neural Networks Current Applications, Chapman & Hall, New York, NY.Google Scholar
  16. Musavi, M. T., Chan, K. H., Hummels, D. M. Kalantri, K. and Ahmed, W. (1992) A probabilistic model for evaluation of neural network classifiers. Pattern Recognition, 25(10), 1241-1251.Google Scholar
  17. Musavi, M. T., Chan, K. H., Hummels, D. M. and Kalantri, K. (1994) On the generalization ability of neural network classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(6), 659-663.Google Scholar
  18. Park, Y. (1994) A comparison of neural net classifiers and linear tree classifiers: their similarities and differences. Pattern Recognition, 27(11), 1493-1503.Google Scholar
  19. Piironen, T., Strommer, E., Silven, O., Laitinen, T. and Pietikaiinen, M. (1989) An automated visual inspection system for rolled metal surfaces. Conference Proceedings of Vision 89, 6-15.Google Scholar
  20. Rao, A. R. (1990) A Taxonomy for Texture Description and Identification, Springer-Verlag, New York, NY.Google Scholar
  21. Ritter, G. X. and Wilson J. N. (1996) Handbook of Computer Vision Algorithms in Image Algebra, CRC Press, Boca Raton, FL.Google Scholar
  22. Schmitt, L. (1990) The practical application of gray-scale morphology to the inspection of surfaces. Conference Proceedings of Vision '90, 8-31.Google Scholar
  23. Song, K. Y., Petrou, M. and Kittler J. (1992) Texture defect detection: a review. Applications of Artificial Intelligence X: Machine Vision and Robotics, SPIE 1708, 99-106.Google Scholar
  24. Suresh, B. R., Fundakowski, R. A., Levitt, T. S. and Overland, J. E. (1983) A real-time automated visual inspection system for hot steel slabs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 5(6), 563-572.Google Scholar
  25. Ventura, J. A. and Chen J. (1996) A structural model for shape recognition using neural nets. Journal of Intelligent Manufacturing, 7, 1-11.Google Scholar
  26. Weszka, J. S., Dyer, C. R. and Rosenfeld, A. (1976) A comparative study of texture measures for terrian classification. IEEE Transactions on Systems, Man, and Cybernetics, 6(4), 269-285.Google Scholar
  27. Zhang, Y. F. and Bresee, R. R. (1995) Fabric defect detection and classification using image analysis. Textile Research Journal, 65(1), 1-9.Google Scholar

Copyright information

© Kluwer Academic Publishers 2000

Authors and Affiliations

  • Choonjong Kwak
    • 1
  • Jose A. Ventura
    • 1
  • Karim Tofang-Sazi
    • 2
  1. 1.Department of Industrial and Manufacturing EngineeringThe Pennsylvania State UniversityUniversity ParkUSA
  2. 2.Westwood IndustriesTupecoUSA

Personalised recommendations