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This research paper focuses on the detection and classification of fabric defects supported digital image processing techniques. The work is intended to supply the upper speed and accuracy of defect detection than human vision and to seek out the supply of the defects. Aiming at the matter for the detection of fabric defect, this paper uses the strategy of Fast Fourier Transform and basic image pre-processing operations to find the material defects; the algorithmic rule planned during this paper will considerably shorten the time of detection on the idea of guaranteeing the proper detection rate. Open CV library and python programming language are employed for the experiment. The experiment result shows that successful defect detection with a 95% accuracy rate, and it's 50% quicker than human vision in materials density calculations.
{"references": ["Chi-ho Chan, Grantham K. H. Pang (2000), \"Fabric Defect Detection by Fourier Analysis,\" IEEE Trans on Industry Application,Volume 36, pp. 513\u2212518", "S.Q. Zhang, D.S. Yu (2004), \"Design and implementation of a parallel real-time FFT processor,\" Proceeding of ICSICT2004, pp. 1665\u22121668", "D.F. Zhang (2009), \"MATLAB digital image processing. Beijing\", Mechanical Industry Press", "Y.L. Jiang, C.F. Xu, (2004), \"Fast Fourier transform FFT and its application\", Photoelectric Engineering Press", "G. Agam (January 27, 2006), \"Introduction to programming with OpenCV\", Department of Computer Science"]}
Fabric defect detection, fast Fourier transform,HSV color models, image processing,open CV, http://matjournals.com/Engineering-Journals.html
Fabric defect detection, fast Fourier transform,HSV color models, image processing,open CV, http://matjournals.com/Engineering-Journals.html
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