A new intelligent fabric defect detection and classification system based on Gabor filter and modified Elman neural network

Author

Zhang, Y.H. ; Yuen, C.W.M. ; Wong, W.K.

Author_Institution

Inst. of Textile & Clothing, HongKong Polytech. Univ., Hong Kong, China

Volume

2

fYear

2010

fDate

27-29 March 2010

Firstpage

652

Lastpage

656

Abstract

In this paper, one fabric defect detection and classification system based on 2D Gabor wavelet transform and Elman neural network is introduced. In the proposed scheme, the texture features of the textile fabric are extracted by using an optimal 2D Gabor filter. A new modified Elman network is proposed to classify the type of fabric defects which have a proportional (P), integral (I) and derivative (D) properties. The proposed inspecting system in this study is more feasible and applicable in fabric defect detection and classification.

Keywords

Gabor filters; fabrics; fault location; feature extraction; image classification; inspection; neural nets; production engineering computing; 2D Gabor filter; 2D Gabor wavelet transform; fabric defect classification system; inspecting system; intelligent fabric defect detection; modified Elman neural network; proportional-integral-derivative properties; textile fabric; texture feature extraction; Clothing; Costs; Fabrics; Gabor filters; Image processing; Inspection; Intelligent networks; Neural networks; Production; Textiles; Elman neural networ; Gabor filter; PID; classification; fabric defect detection;

fLanguage

English

Publisher

ieee

Conference_Titel

Advanced Computer Control (ICACC), 2010 2nd International Conference on

Conference_Location

Shenyang

Print_ISBN

978-1-4244-5845-5

Type

conf

DOI

10.1109/ICACC.2010.5486722

Filename

5486722