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Abstract

In this research, an image system is used as a tool for dynamic inspection of fabrics, and the inspection sample is a piece of plain white fabric. The four defects are holes, oil stains, warp-lacking, and weft-lacking. The image treatment employs a high-resolution linear scan digital camera. Fabric images are acquired first, then the images are transferred to a computer for analysis. Finally, the data are adopted as input data for a neural network, which is obtained from readings after treating the images. In this system, there are three feedforward networks, an input layer, one hidden layer, and an output layer. Because it has the ability to cope with the nonlinear regression property, this method can reinforce the effects of image identification.

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References

Barrett, G.R. , Clapp. T.G. , and Titus, K. , An On-Line Fabric Classification Technique Using a Wavelet-Based Neural Network Approach, Textile Res. J. 66 (8), 521-528 ( 1996).

Bradshaw, M. , The Application of Machine Vision to the Automated Inspection of Knitted Fabrics, Mechatronics 50 (5), 233-243 (1995).

Chen, P.W. , Liang, T.C. , and Yau, H.F. , Classifying Textile Faults with a Back-Propagation Neural Network Using Power Spectra, Textile Res. J. 68 (2), 121-126 ( 1998).

Konda, A. , Xin, L.C. , and Takadera, M. , Evaluation of Pilling by Computer Image Analysis, J. Textile Machin. Soc. Jpn. 36 (3), 96-107 (1988).

Neubauer, C. , Segmentation of Defects in Textile Fabric, IEEE 1, 688-691 (1992).

Ribolzi, S. , Merckle, J. , and Gresser, J. , Real-Time Fault Detection on Textiles Using Opto-electronic Processing , Textile Res. J. 63 (2), 61-71 (1993).

Sanby, C. , Wayne, L.M. , and Harwood, R. , The Automated Inspection of Lace Using Machine Vision, Mecha. tronics 5 (2), 215-231 (1995).

Shin, S.J. , Tsai, I.S. , and Lee, P.D. , Applying the Theorem of Texture "TUNED" Masks on Automatic Faults Detection and Recognition for Static Plain Fabrics, Textile Res. J. 63 (2), 1-10 (1993).

Vangheluwe. L. , Sette, S. , and Pynckels, F. , Assessment of Set Marks by Means of Neural Nets, Textile Res. J. 63 (4), 244-246 (1993).

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Wang, C. , and Huang, S.Z. , A Refined Flexible Inspection Method for Identifying Surface Flaws Using the Skeleton and Neural Network, Int. J. Prod. Res. 35 (9), 2493-2507 (1997 ).

Using a Neural Network to Identify Fabric Defects in Dynamic Cloth Inspection

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