A real-time computer vision system for detecting defects in textile fabrics

This paper proposes a real-time computer vision system for detecting defects in textile fabrics. The developments of both the hardware and software platforms are presented. The design of the prototyped defect detection system ensures that the fabric moves smoothly and evenly so that high quality images can be captured. The paper also proposes a new filter selection method to detect fabric defects, which can automatically tune the Gabor functions to match with the texture information. The filter selection method is further developed into a new defect segmentation algorithm. The scheme is tested both on-line and off-line by using a variety of homogeneous textile images with different defects. The results exhibit accurate defect detection with low false alarm, thus confirming the robustness and effectiveness of the proposed system

[1]  J. Daugman.  Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[2]  Bedrich J. Hosticka,et al.  Unsupervised texture segmentation of images using tuned matched Gabor filters , 1995, IEEE Trans. Image Process..

[3]  Bedrich J. Hosticka,et al.  An unsupervised texture segmentation algorithm with feature space reduction and knowledge feedback , 1998, IEEE Trans. Image Process..

[4]  Ajay Kumar,et al.  Fabric defect segmentation using multichannel blob detectors , 2000 .

[5]  George Papadopoulos,et al.  An approach for automated defect detection and neural classification of web textile fabric , 2000 .

[6]  Jaume Escofet,et al.  Detection of local defects in textile webs using Gabor filters , 1998 .

[7]  K. Srinivasan,et al.  FDAS: A Knowledge-based Framework for Analysis of Defects in Woven Textile Structures , 1992 .

[8]  R. L. de Valois,et al.  Relationship between spatial-frequency and orientation tuning of striate-cortex cells. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[9]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[10]  James S. Goddard,et al.  Vision system for on-loom fabric inspection , 1999 .

[11]  Ajay Kumar,et al.  Defect detection in textured materials using Gabor filters , 2000, Conference Record of the 2000 IEEE Industry Applications Conference. Thirty-Fifth IAS Annual Meeting and World Conference on Industrial Applications of Electrical Energy (Cat. No.00CH37129).