International Journal of Computer
Trends and Technology

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Volume 6 | Number 2 | Year 2013 | Article Id. IJCTT-V6N4P138 | DOI : https://doi.org/10.14445/22312803/IJCTT-V6N4P138

Plain Woven Fabric Defect Detection Based on Image Processing and Artificial Neural Networks

Dr.G.M.Nasira , P.Banumathi

Citation :

Dr.G.M.Nasira , P.Banumathi, "Plain Woven Fabric Defect Detection Based on Image Processing and Artificial Neural Networks," International Journal of Computer Trends and Technology (IJCTT), vol. 6, no. 2, pp. 226-229, 2013. Crossref, https://doi.org/10.14445/22312803/IJCTT-V6N4P138

Abstract

Textile industry is one of the revenues generating industry to TamilNadu. The detection of defect in fabric is a major threat to textile industry. Woven fabrics are produced by weaving. Weaving is a process of interlacing two distinct yarns namely warps and weft. A fabric fault is any abnormality in the fabric that hinders its acceptability by the user. The price of the fabric is affected by the defects in fabric.At present, the fault detection is done manually after production of a sufficient amount of fabric. The nature of work is very dull and repetitive. There is a possibility of human errors with high inspection time in manual inspection, hence it is uneconomical. This paper proposed a computer based inspection system for identification of defects in the woven fabrics using image processing and Artificial Neural Network (ANN) with benefits of low cost and high detection rate. The inspection system first acquires high quality vibration free images of the fabric. Then the acquired images are first preprocessed and normalized using image processing techniques then the preprocessed image is converted into binary images. From the binary image first order statistical features are extracted and these extracted features are given to the input to the Artificial Neural Network (ANN) which uses back propagation algorithm to calculate the weighted factors and generates the output. The ANN is trained by using 115 defect free and defected images.

Keywords

Artificial Neural Network (ANN), Back Propagation Algorithm Fault Detection,, Feature Extraction. Image processing.

References

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