Surface Defect Detection of Rubber Oil Seals Based on Texture Analysis

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 222)

Abstract

The inspection of surface texture is an important part of many industrial quality control applications. The detection of features and classification based on it in a digital image is the key requirement in quality control systems in production and process. An inspection system to replace human inspectors should be capable of detecting flaws such as scratches, stains, (textural defects) and dents, cracks, blow holes (structural defects) occurring in various shapes and sizes. This paper aims at surface defect detection on rubber oil seals based on texture analysis. The proposed method is based on statistical method by extraction of textural features computed from Gray level co-occurrence matrix with different spatial relationships. As the defects were locally concentrated, computing the textural features of the entire image did not prove to be effective, hence the images were divided and then the features were obtained. Also a unique preprocessing method has been proposed and implemented.

Keywords

Surface inspection Textural abnormalities Grey-level co-occurrence matrix 

References

  1. 1.
    Xianghua Xie.: A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques. Electronic Letters on Computer Vision and Image Analysis 7(3):1-22, 2008Google Scholar
  2. 2.
    Jonathan M Blackledge and Dmitry A Dubovitskiy.: A Surface Inspection Machine Vision System that Includes Fractal Texture Analysis. ISAST Transactions on Electronics and Signal ProcessingGoogle Scholar
  3. 3.
    R. Haralick, K. Shanmugan, and I. Dinstein.: Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics. 3(6):610–621, 1973Google Scholar
  4. 4.
    TANG Bo, KONG Jian-yi, WANG Xing-dong and Chen Li.: Surface Inspection System of Steel Strip Based on Machine Vision. First International Workshop on Database Technology and Applications, 359–362, 2009Google Scholar
  5. 5.
    Sina Jahanbin, Alan C. Bovik, Eduardo P`erez,DineshNair.: Automatic Inspection of Textured Surfaces by Support Vector MachinesGoogle Scholar
  6. 6.
    T. Caelli and D. Reye.: On the classification of image regions by colour, texture and shape. Pattern Recognition, 26(4):461–470, 1993Google Scholar
  7. 7.
    J. Chen and A. Jain.: A structural approach to identify defects in textured images. In IEEE International Conference on Systems, Man, and Cybernetics, volume 1, pages 29–32, 1988Google Scholar
  8. 8.

Copyright information

© Springer India 2013

Authors and Affiliations

  • S. Shankar Bharathi
    • 1
  • N. Radhakrishnan
    • 1
  • L. Priya
    • 1
  1. 1.TIFAC CORE in Machine VisionRajalakshmi Engineering CollegeChennaiIndia

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