Fabric Defect Detection Based on SRG-PCNN

Abstract:

Defect segmentation has been a focal point in fabric inspection research, and it remains challenging because it detects delicate features of defects complicated by variations in weave textures and changes in environmental factors. According to the different features between the normal fabric image and defect image, this paper presents an adaptive image segmentation method based on a simplified region growing pulse coupled neural network (SRG-PCNN) for detecting fabric defects. The validation tests on the developed algorithms were performed with fabric images, and results showed that SRG-PCNN is a feasible and efficient method for defect detection.

• Periodical: Advanced Materials Research (Volumes 148-149)
• Pages: 1319-1326
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.148-149.1319
• Online since: October 2010
• Authors: Xiao Shu Si, Hong Zheng, Xue Min Hu
• Keywords: Defect Detection, Fabric, SRG-PCNN

References

[1] C.S. Cho, B.M. Chung and M.J. Park: Development of real-time vision-based fabric inspection system, IEEE Transactions on Industrial Electronics, pp.1073-1079 (2005).

DOI: 10.1109/tie.2005.851648

[2] Ajay Kumar: Computer-Vision-Based Fabric Defect Detection: A Survey, IEEE Transactions on Industrial Electionics, pp.348-363 (2008).

DOI: 10.1109/tie.1930.896476

[3] Jasper, W.J., Gamier, S.J., Potlapalli, H.: Texture characterization and defect detection using adaptive wavelets. Optical Eng. 35(11), pp.3140-3149(1996).

DOI: 10.1117/1.601054

[4] Ajay Kumar, K.H. Pang: Defect Detection in Textured Materials Using Gabor Filters. IEEE Transactions on industry applications. Vol. 38(2). 425-440 (2002).

DOI: 10.1109/28.993164

[5] Ma Y.D., Li L., Wang Y.F., et al: Pulse-Coupled Neural Network's Principles and Applications. Beijing: Science Press, (2006).

[6] Eckhorn R, Reitboeck H J, Arndt M, et al: Feature Linking via Synchronization among Distributed Assemblies : Simulation of Results from Cat Cortex. Neural Computation, 2(3): 293-307 (1990).

DOI: 10.1162/neco.1990.2.3.293

[7] Johnson, J.L., Padgett, M. L: PCNN Models and Applications. IEEE Trans. Neural Networks 10(3), 480-498(1999).

DOI: 10.1109/72.761706

[8] Duan L. J, Lin Z. Q: A method of human skin region detection based on PCNN. Lecture Notes in Computer Science. pp.486-493 (2009).

DOI: 10.1007/978-3-642-01513-7_53

[9] Robert D Stewart, Iris Fermin, Manfred Opper: Region Growing With Pulse-coupled Neural Networks: an alternative to seeded region growing. IEEE Trans. On Neural Network, 13(6): 1557-1562 (2002).

DOI: 10.1109/tnn.2002.804229

[10] Liu, J.Z., Li, W. Q: The automatic thresholding of gray-level pictures via two-dimensional Otsu method. Acta Automat. Sinica 19(1), 101-105 (1993).