Abstract
Fabric inspection is very significant in textile manufacturing. Quality of fabric defends on vital activities of fabric inspection to detect the defects of fabric. Profits of industrialists have been decreased due to fabric defects and cause disagreeable loses. Traditional defect detection methods are conducted in many industries by professional human inspectors who manually draw defect patterns. However, such detection methods have some shortcomings such as exhaustion, tediousness, negligence, inaccuracy, complication as well as time-consuming which cause to reduce the finding of faults. In order to solve these issues, a framework based on image processing has been implemented to automatically and efficiently detect and identify fabric defects. In three steps, the proposed system works. In the first step, image segmentation has been employed on more than a few fabric images in order to enhance the fabric images and to find the valuable information and eliminate the unusable information of the image by using edge detection techniques. After the first step of the paper, morphological operations have been employed on the fabric image. In the third step, feature extraction has been done through FAST (Features from Accelerated Segment Test) extractor. After feature extraction, If PCA (Principal Component Analysis) is applied as it reduces the dimensions and preserves the useful information and classifies the various fabric defects through a neural network and used to find the classification accuracy. The proposed system provides high accuracy as compared to the other system. The investigation has been done in a MATLAB environment on real images of the TILDA database.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Stojanovic, R., Mitropulos, P., Koulamas, C., Karayiannis, Y., Koubias, S., Papadopoulos, G.: Real-time vision-based system for textile fabric inspection. Real-Time Imaging 7, 507–518 (2001)
Aasim A.: A catalogue of visual textile defects, ministry of textiles (2004)
Newman, T.S., Jain, A.K.: A survey of automated visual inspection. Comput. Vis. Image Underst. 61(2), 231–262 (1995)
Kumar, A.: Computer-vision-based fabric defect detection: a survey. IEEE Trans. Ind. Electron. 55(1), 348–363 (2008)
Huart, J., Postaire, J.G.: Integration of computer vision on to weavers for quality control in the textile industry. In: Proceeding SPIE 2183, pp. 155–163, February 1994
Dorrity, J.L., Vachtsevanos, G.: On-line defect detection for weaving systems. In: Proceeding IEEE Annual Technical Conference Textile, Fiber, and Film Industry, pp. 1–6, May 1996
Ryan G.: Rosandich: Intelligent Visual Inspection, Chapman & Hall, London (U.K.) (1997)
Batchelor, B.G.: Lighting and viewing techniques. In: Batchelor, B.G., Hill, D.A., Hodgson, D.C. (eds) Automated Visual Inspection. IFS and North Holland (1985)
Roberts, J.W., Rose, S.D., Jullian, G., Nicholas, L., Jenkins, P.T., Chamberlin, S.G., Maroscher, G., Mantha, R., Litwiller, D.J.: A PC-based real time defect imaging system for high speed web inspection. In:Proceeding SPIE 1907, pp. 164–176 (1993)
Bayer, H.A.: Performance analysis of CCD-cameras for industrial inspection. In: Proceeding SPIE 1989, pp. 40–49 (1993)
Cho, C., Chung, B., Park, M.: Development of real-time vision-based fabric inspection system. IEEE Trans. Ind. Electron. 52(4), 1073–1079 (2005)
Kumar, A.: Computer-vision-based fabric defect detection: a survey. IEEE Trans. Ind. Electron. 55(1), 348–363 (2008)
Ngana, H., Panga, G., Yung, N.: Automated fabric defect detection a review. Image Visi. Comput. 29(7), 442–458 (2011)
Smith, B.: Making war on defects. IEEE Spectr. 30(9), 43–47 (1993)
Fernandez, C., Fernandez, S., Campoy P., Aracil R.: On-line texture analysis for flat products inspection. neural nets implementation. In: Proceedings of 20th IEEE International Conference on Industrial Electronics, Control and Instrumentation, vol. 2, pp. 867–872(1994)
Ozdemir S., Ercil A.: Markov random fields and Karhunen-Loeve transforms for defect inspection of textile products. In: IEEE Conference on Emerging Technologies and Factory Automation, vol. 2, pp. 697–703 (1996)
Bodnarova A., Williams J. A., Bennamoun M., Kubik K. Optimal textural features for flaw detection in textile materials. In: Proceedings of the IEEE TENCON 1997 Conference, Brisbane, Australia, pp. 307–310 (1997)
Gong, Y.N.: Study on image analysis of fabric defects. Ph.D. dissertation, China Textile University, Shanghai China (1999)
Zhang, Y.F., Bresee, R.R.: Fabric defect detection and classification using image analysis. Text. Res. J. 65(1), 1–9 (1995)
Nickolay, B., Schicktanz, K., Schmalfub, H.: Automatic fabric inspection– utopia or reality. Trans. Melliand Textilberichte 1, 33–37 (1993)
Habib, M.T., Rokonuzzaman, M.: A set of geometric features for neural network-based textile defect classification, ISRN Artif. Intell. 2012, Article ID 643473, p. 16 (2012)
Saeidi, R.D., Latifi, M., Najar, S.S., Ghazi Saeidi, A.: Computer Vision-Aided Fabric Inspection System For On-Circular Knitting Machine, Text. Res. J. 75(6), 492–497 (2005)
Islam, M.A., Akhter, S., Mursalin, T.E.: Automated textile defect recognition system using computer vision and artificial neural networks. In: Proceedings World Academy of Science, Engineering and Technology, vol. 13, pp. 1–7, May 2006
Murino, V., Bicego, M., Rossi, I.A.: Statistical classification of raw textile defects. In: 17th International Conference on Pattern Recognition (ICPR 2004), ICPR, vol. 4, pp. 311–314 (2004)
Karayiannis, Y.A., Stojanovic, R., Mitropoulos, P., Koulamas, C., Stouraitis, T., Koubias, S., Papadopoulos, G.: Defect detection and classification on web textile fabric using multi resolution decomposition and neural networks. In: Proceedings on the 6th IEEE International Conference on Electronics, Circuits and Systems, Pafos, Cyprus, pp. 765–768, September 1999
Kumar, A.: Neural network based detection of local textile defects. Pattern Recogn. 36, 1645–1659 (2003)
Kuo, C.F.J., Lee, C.-J.: A back-propagation neural network for recognizing fabric defects. Text. Res. J. 73(2), 147–151 (2003)
Mitropoulos, P., Koulamas, C., Stojanovic, R., Koubias, S., Papadopoulos, G., Karayiannis, G.: Real-time vision system for defect detection and neural classification of web textile fabric. In: Proceedings SPIE, vol. 3652, San Jose, California, pp. 59–69, January 1999
Shady, E., Gowayed, Y., Abouiiana, M., Youssef, S., Pastore, C.: Detection and classification of defects in knitted fabric structures. Text. Res. J. 76(4), 295–300 (2006)
Campbell, J.G.,. Fraley, C., Stanford, D., Murtagh, F., Raftery, A.E.: Model-based methods for textile fault detection, Int. J. Imaging Syst. Technol. 10(4), 339–346, July 1999
Islam, M.A., Akhter, S., Mursalin, T.E., Amin, M.A.: A suitable neural network to detect textile defects. Neural Inf. Process. 4233, 430–438. Springer, October 2006
Habib, M.T., Rokonuzzaman, M.: Distinguishing feature selection for fabric defect classification using neural network. J. Multimedia 6 (5), 416–424, October 2011
TILDA Textile texture database, texture analysis working group of DFG. http://lmb.informatik.unifreiburg.de
Srinivasan, K., Dastor, P. H., Radhakrishnaihan, P., Jayaraman, S.: FDAS: a knowledge-based frame detection work for analysis of defects in woven textile structures, J. Text. Inst. 83(3), 431–447 (1992)
Rao Ananthavaram, R.K., Srinivasa, Rao O., Krishna P.M.H.M.: Automatic defect detection of patterned fabric by using RB method and independent component analysis. Int. J. Comput. Appl. 39(18), 52–56 (2012)
Sengottuvelan, P., Wahi, A., Shanmugam, A.: Automatic fault analysis of textile fabric using imaging systems. Res. J. Appl. Sci. 3(1), 26–31 (2008)
Abdi. H., Williams, L.J.: Principal component analysis. Wiley Interdisciplinary Rev. Comput. Stat. 2 (4), 433–459 (2010). https://doi.org/10.1002/wics.101
Kumar, T., Sahoo, G.: Novel method of edge detection using cellular automata. Int. J. Comput. Appl. 9(4), 38–44 (2010)
Zhu, Q.: Efficient evaluations of edge connectivity and width uniformity. Image Vis. Comput. 14, 21–34 (1996)
Senthilkumaran. N., Rajesh, R.: Edge detection techniques for image segmentation – a survey of soft computing approaches. Int. J. Recent Trends Eng. 1(2), 250–254 (2009)
Rizon, M., Hashim, M.F., Saad, P., Yaacob, S.: Face recognition using eigen faces and neural networks. Am. J. Appl. Sci. 2(6), 1872–1875 (2006)
Rosten, E., Porter, R., Drummond,T.: FASTER and better: a machine learning approach to corner detection, IEEE Trans Pattern Anal Mach Intell. 32, 105–119 (2010)
Wikipedia, Corner Detection. http://en.wikipedia.org/wiki/Corner_detection. Accessed 16 March 2011
Chang, J.Y., Chen, J.L.: Automated facial expression recognition system using neural networks. J. Chin. Inst. Eng. 24(3), 345–356 (2001)
Jianli, L., Baoqi, Z.: Identification of fabric defects based on discrete wavelet transform and back-propagation neural network. J. Text. Inst. 98(4), 355–362 (2007)
Tamnun, M.E., Fajrana, Z.E., Ahmed, R.I.: Fabric defect inspection system using neural network and microcontroller. J. Theor. Appl. Inf. Technol. 4(7) (2008)
Bhanumati, P., Nasira, G.M.: Fabric inspection system using artificial neural network. Int. J. Comput. Eng. 2(5), 20–27 May 2012
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Mahmud, T., Sikder, J., Chakma, R.J., Fardoush, J. (2021). Fabric Defect Detection System. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing and Optimization. ICO 2020. Advances in Intelligent Systems and Computing, vol 1324. Springer, Cham. https://doi.org/10.1007/978-3-030-68154-8_68
Download citation
DOI: https://doi.org/10.1007/978-3-030-68154-8_68
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-68153-1
Online ISBN: 978-3-030-68154-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)