An Effective Automatic Fabric Defect Detection System using Digital Image Processing
J. Environ. Nanotechnol., Volume 6, No 1 (2017) pp. 79-85
Abstract
For a long time fabric defect detection is carried out manually with human visual inspection. Automatic fabric inspection is important to maintain the quality of fabric. Fabric analysis is performed on the basis of digital images of the fabric. The recognizer acquires digital fabric images by image acquisition device and sends it to a computer system to processes the received image. The computer system makes a fabric analysis to find out whether the fabric is defect free or defected using Digital image processing techniques. In spite of innumerable algorithms available, the research is still challenging one. This paper presents the need, challenges and the processes of automatic fabric defect detection system. And more over the paper presents all the possible available technologies involved in the automatic fabric defect detection system.
Full Text
Reference
Banumathi, P. and Nasira, G. M., Fabric inspection system using artificial neural networks, Inter. J. Computer Eng. Sci., (IJCES), 2(5), 20-27(2012).
Banumathi, P. and Nasira, G. M., Fourier transform and image processing in automated fabric defect inspection system, Int. J. Comput. Int. Info., (IJCII), 3(1), 61-64(2013).
Bodnarova, A., Bennamoun, M. and Kubik, K., Suitability analysis of techniques for flaw detection in textiles using texture analysis, Pattern Analysis and Applications, 3(3), 254-266(2000).
doi:10.1007/s100440070010
Brzakovic, D. and Vujovic, N., Designing defect classification systems: a case study, Pattern Recognition, 29(8), 1401-1419(1996).
doi:10.1016/0031-3203(95)00166-2
Cho, C. S., Chung, B. M. and Park, M. J., Development of real-time vision-based fabric inspection system, IEEE Trans. on Industrial Electronics, 52(4), 1073-1079(2005).
doi:10.1109/TIE.2005.851648
Conci, A. and Proença, C., A Computer vision approach for textile inspection, Tex. Res. J., 70(4), (2000).
Fatemi-Ghomi, N., Palmer, P. L. and Petrou, M., Performance evaluation of texture segmentation algorithms based on wavelets, Proceedings of the Workshop on Performance Characteristics of Vision Algorithms (ECCV), Cambridge, (1996).
Gonzales, R. and Woods, R., Digital image processing, Prentice Hall, (2008).
Jansi, S. and Subhashini, P., Particle swarm optimization total variation filter for image denoising, J. Theor. Appl. Infor. Technol., 57(2), 169-173(2013).
Jia He and Qian Jiang, Research on the fabric defect detection method based on improved PSO and NN algorithm, Int. J. Dig. Cont. Technol. Appl.(JDCTA), 6(8), 177-184(2012).
doi:10.4156/jdcta.vol6.issue8.21
Kumar, T. A., Paul. V. and Priya, S., A novel approach to fabric defect detection using digital image processing, Signal Processing, Communication, Computing and Networking Technologies(ICSCCN), (2011).
doi:10.1109/ICSCCN.2011.6024549
Mak, K. L., Peng, P. and Yiu, K. F. C., Fabric defect detection using morphological filters, Image and Vision Computing, 27(10), 1585–1592(2009).
doi:10.1016/j.imavis.2009.03.007
Nasira, G. M. and Banumathi, P., Automatic defect detection algorithm for woven fabric using artificial neural network techniques, Int. J. Innov. Res. Comp. Comm. Eng.(IJICRCCE), 2(1), 2620-2624(2014).
Priyanka Vyas and Manish Kakhani, Fabric fault processing using image processing techniques, Int. J. Mult. Res. Dev., 2(2), 29-31(2015).
Sengottuvelan, P., Wahi, A. and Shanmugam, A., Automatic fault analysis of textile fabric using imaging systems, Res. J. Appl. Sci., 3(1), 26-31(2008).
Srinivasan, K., Dastor, P. H., Radhakrishnaihan, P. and Jayaraman, S., FDAS: A knowledge-based frame detection work for analysis of defects in woven textile structures, J. Textile Institute, 83(3), 431-447(1992).
Umbough, S. E., Computer vision and image processing, Englewood Cliffs, Nj: Prentice- Hall(1998). Zhang, Y. F. and Bresee, R. R, Fabric defect detection and classification using image analysis, Tex. Res. J., 65, (1995).
