Defect Detection in Fabrics Using Back Propagation Neural Networks
Gnanaprakash V1, Vanathi P T2, Suresh G3
1Gnanaprakash V, Department of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Sathya Mangalam, Tamil Nadu, India.
2Vanathi P T, Department of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Sathya Mangalam, Tamil Nadu, India.
3Suresh G, Department of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Sathya Mangalam, Tamil Nadu, India.
Manuscript received on 01 December 2018 | Revised Manuscript received on 06 December 2018 | Manuscript Published on 26 December 2018 | PP: 132-138 | Volume-8 Issue- 2S2 December 2018 | Retrieval Number: BS2027128218/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Defect detection in Fabrics plays an significant role in automatic defect detection system in textile industries. Identification of fabric fault mainly include three parts: The first, preprocessing with Frequency domain Butterworth Low pass Filter and Histogram Equalization. The second, texture features extraction of fabric with Gray Level Co-occurrence Matrix (GLCM).The GLCM characterizes the distribution of co-occurring pixel values in an image to be at a given offset, and then the statistical texture features are obtained from this GLCM. Third, the fault is identified using Back Propagation Neural Network with different combinations of GLCM features as an input.
Keywords: Back Propagation Neural Network, Butterworth Low Pass Filter, Gray Level Co-Occurrence Matrix, Histogram Equalization.
Scope of the Article: Communication