Defect Detection in Fabric using Wavelet Transform and Genetic Algorithm

Authors

  • Depavath Harinath Dept. of Computer Science, HRD Degree and P.G College Narayanaguda, Hyderabad,Telangana,India.
  • K. Ramesh Babu Dept. of Mathematics, M.V.S.R Engineering College,Nadergul,R.R.District,Hyderabad,Telangana,India.
  • P. Satyanarayana Dept. of Computer Science, Osmania University, Hyderabad,Telangana,India.
  • M.V. Ramana Murthy Dept. of Computer Science, Osmania University, Hyderabad,Telangana,India.

DOI:

https://doi.org/10.14738/tmlai.36.1551

Keywords:

Wavelet transform, Genetic Algorithm, Fabric Defect Detection.

Abstract

Fabric defect detection is one of the indispensible units in the manufacturing industry to maintain the quality of the  end  product.  Wavelet  transform  is  well  suited  for  quality  inspection  application  due  to  its  multi-resolution  representation and to extract fabric features. In this paper a new scheme is proposed for fabric defect detection in textile industry. For this purpose, all coefficients were extracted from perfect fabric. These coefficients can defect main fabric image & indicate defects of fabric textile by optimal subset of these coefficients. For finding defects a suitable subset of Genetic Algorithm is used in this process. The Shannon entropy is used as evaluation function in Genetic Algorithm By using two separable sets of wavelet coefficients for horizontal and vertical defects, it was seen that we get better results for defect detection. The advantage of this approach is that it improves accuracy of fabric defect detection as well decreases computation time.

Author Biography

Depavath Harinath, Dept. of Computer Science, HRD Degree and P.G College Narayanaguda, Hyderabad,Telangana,India.

Department of Computer Science

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Published

2016-01-03

How to Cite

Harinath, D., Babu, K. R., Satyanarayana, P., & Murthy, M. R. (2016). Defect Detection in Fabric using Wavelet Transform and Genetic Algorithm. Transactions on Engineering and Computing Sciences, 3(6), 10. https://doi.org/10.14738/tmlai.36.1551