Skip to main content
SpringerLink
Log in

An analytical survey of textile fabric defect and shade variation detection system using image processing

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In modern days the detection of defects in textile industries using digital image processing techniques is an emerging area of research. The faulty fabric is subjected to several image processing techniques such as preprocessing, feature identification, segmentation and classification. The detection in the fabric are identified through manual inspection which is highly difficult because of the significant number of fabric defect groups distinguished by their vagueness and ambiguity. Thus considering the effectiveness of detection and the labor cost, there is a need for automated system for the identification of fabric defects. Several techniques for detecting fabric defects and shade variation have been developed by various researchers. The aim of the paper is to present the detailed review of the techniques and algorithms developed for finding the defects and shade variation in the fabric. Totally, 79 papers have been reviewed and the results are compared to identify the best suited method for fabric defect detection. This paper compares the various techniques used by various researchers, the state-of- the-art, pros and cons of the techniques, the background of the proven findings and their detection ratio over the past three years i.e. 2017–2020. From the survey, it is analyzed that the deep learning approach gives the highest detection accuracy than other methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data availability

All the details about data availability are mentioned within this manuscript.

References

  1. Anandan P, Sabeenian RS (2018) Fabric defect detection using discrete Curvelet transform. International conference on robotics and smart manufacturing (RoSMa2018). Procedia Comput Sci 133:1056–1065. https://doi.org/10.1016/j.procs.2018.07.058

    Article  Google Scholar 

  2. Aziz MA, Haggag AS, Sayed MS (2013) Fabric defect detection algorithm using morphological processing and DCT. 1st international conference on communications, signal processing, and their applications (ICCSPA), Sharjah, pp. 1-4. https://doi.org/10.1109/ICCSPA.2013.6487269

  3. Bai F, Fan M, Yang H, Dong L (2012) Image segmentation method for coal particle size distribution analysis. Particuology 56:163–170. https://doi.org/10.1016/j.partic.2020.10.002

    Article  Google Scholar 

  4. Bandara P, Bandara T, Ranatunga T, Vimarshana V, Sooriyaarachchi S, Silva CD (2018) Automated fabric defect detection. 18th international conference on advances in ICT for emerging regions (ICTer), Colombo, Sri Lanka, 2018, pp. 119–125. https://doi.org/10.1109/ICTER.2018.8615491

  5. Biradar MS, Sheeparmatti BG, Patil PM, Ganapati Naik S (2017) Patterned fabric defect detection using regular band and distance matching function. International Conference on Computing, Communication, Control and Automation (ICCUBEA), Pune, pp. 1–6. https://doi.org/10.1109/ICCUBEA.2017.8463904

  6. Chandrasekaran V, Sanghavi S, Parrilo PA, Willsky AS (2019) Sparse and low-rank matrix decompositions. Elsevier IFAC Proc 42(10):1493–1498. https://doi.org/10.3182/20090706-3-FR-2004.00249

    Article  Google Scholar 

  7. Chang X, Chengxi G, Liang J, Xu X (2018) Fabric defect detection based on pattern template correction. Math Probl Eng 2018:01–17. https://doi.org/10.1155/2018/3709821

    Article  Google Scholar 

  8. Choi Y, Sharifahmadian E, Latifi S (2013) Performance analysis of contourlet-based hyperspectral image fusion methods. Int J Inf Theory 2(1/2/3/4):01–14. https://doi.org/10.5121/ijit.2014.2401

    Article  Google Scholar 

  9. Cui F-Y, Zou L-J, Song B (2008) Edge feature extraction based on digital image processing techniques. IEEE International Conference on Automation and Logistics, Qingdao, pp. 2320–2324. https://doi.org/10.1109/ICAL.2008.4636554

  10. Deotale NT, Sarode TK (2019) Fabric defect detection adopting combined GLCM, Gabor wavelet features and random decision forest. 3D Res 10(5):01–13. https://doi.org/10.1007/s13319-019-0215-1

    Article  Google Scholar 

  11. Do MN, Vetterli M (2005) The contourlet transform: an efficient directional multiresolution image representation. IEEE Trans Image Process 14(12):2091–2106. https://doi.org/10.1109/TIP.2005.859376

    Article  Google Scholar 

  12. Gao G, Liu C, Liu Z, Li C, Yang R (2017) Fabric defect detection based on Gabor filter and tensor low-rank recovery. 4th IAPR Asian conference on pattern recognition (ACPR), Nanjing, pp. 73-78. https://doi.org/10.1109/ACPR.2017.37

  13. Guan S (2018) Fabric defect delaminating detection based on visual saliency in HSV color space. J Text Inst 109(12):1560–1573. https://doi.org/10.1080/00405000.2018.1434112

    Article  Google Scholar 

  14. Guan S, Shi H (2017) Fabric defect detection based on the saliency map construction of target-driven feature. J Text Inst 109(9):1133–1142. https://doi.org/10.1080/00405000.2017.1414669

    Article  Google Scholar 

  15. Guan M, Zhong Z, Rui Y (2019) Automatic defect segmentation for plain woven fabric images. International Conference on Communications, Information System and Computer Engineering (CISCE), Haikou, China, pp. 465–468. https://doi.org/10.1109/CISCE.2019.00108

  16. Guan M, Zhong Z, Rui Y, Zheng H, Wu X (2019) Defect detection and classification for plain woven fabric based on deep learning. Seventh international conference on advanced cloud and big data (CBD), Suzhou, China, pp. 297-302. https://doi.org/10.1109/CBD.2019.00060

  17. Habib M, Faisal RH, Rokonuzzaman M, Ahmed F (2014) Automated fabric defect inspection: a survey of classifiers. Int J Found Comput Sci Technol 4(1):17–25. https://doi.org/10.5121/ijfcst.2014.4102

    Article  Google Scholar 

  18. Hamdi AA, Fouad MM, Sayed MS, Hadhoud MM (2017) Patterned fabric defect detection system using near infrared imaging. Eighth international conference on intelligent computing and information systems (ICICIS), Cairo, pp. 111-117. https://doi.org/10.1109/INTELCIS.2017.8260041

  19. Hamdi AA, Sayed MS, Fouad MM, Hadhoud MM (2018) Unsupervised patterned fabric defect detection using texture filtering and K-means clustering. International conference on innovative trends in computer engineering (ITCE), Aswan, pp. 130-144. https://doi.org/10.1109/ITCE.2018.8316611

  20. Hanbay K, Talu MF, Özgüven ÖF, Öztürk D (2015) Fabric defect detection methods for circular knitting machines. 23nd signal processing and communications applications conference (SIU), Malatya, pp. 735-738. https://doi.org/10.1109/SIU.2015.7129932

  21. Hanbay K, Talu MF, Özgüven ÖF (2016) Fabric defect detection systems and methods - a systematic literature review. Int J Light Electron Opt 127(24):11960–11973. https://doi.org/10.1016/j.ijleo.2016.09.110

    Article  Google Scholar 

  22. Hanbay K, Golgiyaz S, Talu MF (2017) Real time fabric defect detection system on Matlab and C++/Opencv platforms .International Artificial Intelligence and Data Processing Symposium (IDAP), Malatya, pp. 1–8. https://doi.org/10.1109/IDAP.2017.8090180

  23. Hu MC, Tsai IS (2000) The inspection of fabric defects by using wavelet transform. J Text Inst 91(3):420–433. https://doi.org/10.1080/00405000008659518

    Article  Google Scholar 

  24. Huangpeng Q, Zhang H, Zeng X, Huang W (2018) Automatic visual defect detection using texture prior and low-rank representation. IEEE Access 6:37965–37976. https://doi.org/10.1109/ACCESS.2018.2852663

    Article  Google Scholar 

  25. Jaafar NHN (2020) Discrete Curvelet transform algorithm for image compression system. Int J Adv Trends Comput Sci Eng 9(1.1 S I):166–169. https://doi.org/10.30534/ijatcse/2020/3091.12020

    Article  Google Scholar 

  26. Javed A, Mirza AU (2013) Comparative analysis of different fabric defects detection techniques. Int J Image Graph Signal Process 5(1):40–45. https://doi.org/10.5815/ijigsp.2013.01.06

    Article  Google Scholar 

  27. Jia L, Chen C, Liang J, Hou Z (2017) Fabric defect inspection based on lattice segmentation and Gabor filtering. Neurocomputing 238(17):84–102. https://doi.org/10.1016/j.neucom.2017.01.039

    Article  Google Scholar 

  28. Kaynar O, Işik YE, Görmez Y, Demirkoparan F(2017) Fabric defect detection with LBP-GLMC. International Artificial Intelligence and Data Processing Symposium (IDAP), Malatya, pp. 1–5. https://doi.org/10.1109/IDAP.2017.8090188

  29. Khar A (2018) Green apparel buying behaviour: opportunities in Indian market. Trends Text Eng Fash Technol 3(1):271–275. https://doi.org/10.31031/tteft.2018.03.000555

    Article  Google Scholar 

  30. Kumar A (2003) Neural network based detection of local textile defects. Elsevier Pattern Recog 36(7):1645–1659. https://doi.org/10.1016/S0031-3203(03)00005-0

    Article  Google Scholar 

  31. Kumar A (2008) Computer-vision-based fabric defect detection: a survey. IEEE Trans Ind Electron 55(1):348–363. https://doi.org/10.1109/TIE.1930.896476

    Article  Google Scholar 

  32. Kuo CFJ, Lee CJ, Tsai CC (2003) Using a neural network to identify fabric defects in dynamic cloth inspection. Text Res J 73(3):238–244. https://doi.org/10.1177/004051750307300307

    Article  Google Scholar 

  33. Kure N, Biradar MS, Bhangale KB (2017) Local neighborhood analysis for fabric defect detection. International Conference on Information, Communication, Instrumentation and Control (ICICIC), Indore, pp. 1–5. https://doi.org/10.1109/ICOMICON.2017.8279095

  34. Li Y, Cheng Z (2016) Automated vision system for fabric defect inspection using Gabor filters and PCNN. Springer Plus 5(765):01–12. https://doi.org/10.1186/s40064-016-2452-6

    Article  Google Scholar 

  35. Li Y, ZhaoW PJ (2017) Deformable patterned fabric defect detection with fisher criterion-based deep learning. IEEE Trans Autom Sci Eng 14(2):1256–1264. https://doi.org/10.1109/TASE.2016.2520955

    Article  Google Scholar 

  36. Li N, Bi H, Zheng Z, Kong X, Lu D (2018) Performance comparison of saliency detection. Adv Multimedia 2018:01–13. https://doi.org/10.1155/2018/9497083

    Article  Google Scholar 

  37. Li Y, Luo H, Yu M, Jiang G, Cong H (2018) Fabric defect detection algorithm using RDPSO-based optimal Gabor filter. J Text Inst 110(4):487–495. https://doi.org/10.1080/00405000.2018.1489951

    Article  Google Scholar 

  38. Li Y, Dong Z, Lee D-J (2018) Automatic fabric defect detection with a wide-and-compact network. Neurocomputing 329(15):329–338. https://doi.org/10.1016/j.neucom.2018.10.070

    Article  Google Scholar 

  39. Li C, Gao G, Liu Z, Yu M, Huang D (2018) Fabric defect detection based on biological vision modeling. IEEE Access 6:27659–27670. https://doi.org/10.1109/ACCESS.2018.2841055

    Article  Google Scholar 

  40. Li C, Gao G, Liu Z, Huang D, Xi J (2019) Defect detection for patterned fabric images based on GHOG and low-rank decomposition. IEEE Access 7:83962–83973. https://doi.org/10.1109/ACCESS.2019.2925196

    Article  Google Scholar 

  41. Liang J, Zhang J, Chen S, Hou Z (2018) Fabric defect inspection based on lattice segmentation and lattice templates. J Frankl Inst 355(15):7764–7798. https://doi.org/10.1016/j.jfranklin.2018.07.005

    Article  MATH  Google Scholar 

  42. Liang J, Chen C, Xu S, Shen J (2020) Fabric defect inspection based on lattice segmentation and template statistics. Inf Sci 512:964–984. https://doi.org/10.1016/j.ins.2019.10.032

    Article  Google Scholar 

  43. Liu Z, Wang B, Li C, Li B, Liu X (2017) Fabric defect detection algorithm based on convolution neural network and low-rank representation. 4th IAPR Asian conference on pattern recognition (ACPR), Nanjing, pp. 465-470. https://doi.org/10.1109/ACPR.2017.34

  44. Liu L, Ouyang W, Wang X, Fieguth P, Chen J, Liu X, Pietikäinen M (2020) Deep learning for generic object detection: a survey. Int J Comput Vis 128:261–318. https://doi.org/10.1007/s11263-019-01247-4

    Article  MATH  Google Scholar 

  45. Lizarraga-Morales RA, Correa-Tome FE, Sanchez-Yanez RE, Cepeda-Negrete J (2019) On the use of binary features in a rule-based approach for defect detection on patterned textiles. IEEE Access 7:18042–18049. https://doi.org/10.1109/ACCESS.2019.2896078

    Article  Google Scholar 

  46. Mak KL, Peng P, Yiu KFC (2009) Fabric defect detection using morphological filters. Elsevier Image Vis Comput 27(10):1585–1592. https://doi.org/10.1016/j.imavis.2009.03.007

    Article  Google Scholar 

  47. Ngan HYT, Pang GKH, Yung NHC (2011) Automated fabric defect detection. Elsevier Image Vis Comput 29(7):442–458. https://doi.org/10.1016/j.imavis.2011.02.002

    Article  Google Scholar 

  48. Ngan HYT, Pang GKH, Yung NHC (2011) Automated fabric defect detection—a review. Image Vis Comput 29(7):442–458. https://doi.org/10.1016/j.imavis.2011.02.002

    Article  Google Scholar 

  49. Ouyang W, Xu B, Hou J, Yuan X (2019) Fabric defect detection using activation layer embedded convolutional neural network. IEEE Access 7:70130–70140. https://doi.org/10.1109/ACCESS.2019.2913620

    Article  Google Scholar 

  50. Pan Z, He N, Jiao Z (2017) FFT used for fabric defect detection based on CUDA. IEEE 2nd advanced information technology, electronic and automation control conference (IAEAC), Chongqing, pp. 2104-2107. https://doi.org/10.1109/IAEAC.2017.8054389

  51. Peng D, Zhong G, Rao Z, Shen T, Chang Y, Wang M (2018) A fast detection scheme for original fabric based on blob, canny and rotating integral algorithm. IEEE 3rd international conference on image, vision and computing (ICIVC), Chongqing, pp. 113-118. https://doi.org/10.1109/ICIVC.2018.8492813

  52. Priya S, Ashok Kumar T, Paul V (2011) A novel approach to fabric defect detection using digital image processing. 2011 International conference on signal processing, Communication, Computing and Networking Technologies, Thuckafay, pp. 228–232. https://doi.org/10.1109/icsccn.2011.6024549

  53. Rebhi A, Benmhammed I, Abid S, Fnaiech F (2015) Fabric defect detection using local homogeneity analysis and neural network. J Photon 2015:01–09. https://doi.org/10.1155/2015/376163

    Article  Google Scholar 

  54. Ren Z, Fang F, Yan N et al (2021) State of the art in defect detection based on machine vision. Int J Precis Eng Manuf-Green Technol. https://doi.org/10.1007/s40684-021-00343-6

  55. Sadaghiyanfam S (2018) Using gray-level-co-occurrence matrix and wavelet transform for textural fabric defect detection: a comparison study. Electric electronics, computer science, biomedical Engineerings' meeting (EBBT), Istanbul, pp. 1-5. https://doi.org/10.1109/EBBT.2018.8391440

  56. Şeker A (2018) Evaluation of fabric defect detection based on transfer learning with pre-trained AlexNet. International conference on artificial intelligence and data processing (IDAP), Malatya, Turkey, pp. 1-4. https://doi.org/10.1109/IDAP.2018.8620888

  57. Senthilkumar M (2014) Use of artificial neural networks (ANNs) in colour measurement. Colour Measurement, Principles, Advances and Industrial Applications, Woodhead Publishing Series in Textiles, pp.125–146. https://doi.org/10.1533/9780857090195.1.125

  58. Shah R, Gao Z, Mittal H (2015) Chapter 18 - impact on the economy, innovation, entrepreneurship, and the economy in the US, China, and India historical perspectives and future trends. Elsevier Academic Press, New York, pp 293–300. https://doi.org/10.1016/C2014-0-01381-0

    Book  Google Scholar 

  59. Shi B, Liang J, Di L, Chen C, Hou Z (2019) Fabric defect detection via low-rank decomposition with gradient information. IEEE Access 7:130423–130437. https://doi.org/10.1109/ACCESS.2019.2939843

    Article  Google Scholar 

  60. Silvestre-Blanes J, Albero-Albero T, Miralles I, Pérez-Llorens R, Moreno J (2019) A public fabric database for defect detection methods and results. AUTEX Res J 19(4):363–374. https://doi.org/10.2478/aut-2019-0035

    Article  Google Scholar 

  61. Tian H, Li F (2019) Autoencoder-based fabric defect detection with cross- patch similarity. 16th international conference on machine vision applications (MVA), Tokyo, Japan, pp. 1-6. https://doi.org/10.23919/MVA.2019.8758051

  62. Tilocca A, Borzone P, Carosio S, Durante A (2002) Detecting fabric defects with a neural network using two kinds of optical patterns. Text Res J 72(6):545–550. https://doi.org/10.1177/004051750207200614

    Article  Google Scholar 

  63. Tong L, Wong WK, Kwong CK (2017) Fabric defect detection for apparel industry: a nonlocal sparse representation approach. IEEE Access 5:5947–5964. https://doi.org/10.1109/ACCESS.2017.2667890

    Article  Google Scholar 

  64. Üzen H, Firat H, Karcı A, Hanbay D (2019) Automatic thresholding method developed with entropy for fabric defect detection. International artificial intelligence and data processing symposium (IDAP), Malatya, Turkey, pp. 1-4. https://doi.org/10.1109/IDAP.2019.8875890

  65. Vladimir G, Evgen I, Aung NL (2019) Automatic detection and classification of weaving fabric defects based on digital image processing. IEEE conference of Russian young researchers in electrical and electronic engineering (EIConRus), Saint Petersburg and Moscow, Russia, pp. 2218-2221. https://doi.org/10.1109/EIConRus.2019.8657318

  66. Wang J, Li Q, Gan J, Yu H (2017) Fabric defect detection based on improved low-rank and sparse matrix decomposition. IEEE International Conference on Image Processing (ICIP), Beijing, pp. 2776-2780. https://doi.org/10.1109/ICIP.2017.8296788

  67. Wang J, Li C, Liu Z, Yu M, Dong Y (2018) A novel patterned fabric defect detection algorithm based on dual norm low rank decomposition. 14th IEEE international conference on signal processing (ICSP), Beijing, China, pp. 323-327. https://doi.org/10.1109/ICSP.2018.8652495

  68. Weninger L, Kopaczka M, Merhof D (2018) Defect detection in plain weave fabrics by yarn tracking and fully convolutional networks. IEEE international instrumentation and measurement technology conference (I2MTC), Houston, TX, pp. 1-6. https://doi.org/10.1109/I2MTC.2018.8409546

  69. Wijesingha D, Jayasekara B (2018) Detection of defects on warp-knit fabric surfaces using self organizing map. Moratuwa engineering research conference (MERCon), Moratuwa, pp. 601-606. https://doi.org/10.1109/MERCon.2018.8421944

  70. Yapi D, Allili MS, Baaziz N (2018) Automatic fabric defect detection using learning-based local textural distributions in the Contourlet domain. IEEE Trans Autom Sci Eng 15(3):1014–1026. https://doi.org/10.1109/TASE.2017.2696748

    Article  Google Scholar 

  71. Yazan E, Çelik G, Talu MF, Yeroğlu C (2018) Vortex optimization algorithm based fabric defect detection. International conference on artificial intelligence and data processing (IDAP), Malatya, Turkey, pp. 1-6. https://doi.org/10.1109/IDAP.2018.8620911

  72. Zhang YH, Yuen CWM, Wong W, Kan C-w (2011) An intelligent model for detecting and classifying color-textured fabric defects using genetic algorithms and the Elman neural network. Text Res J 81(17):1772–1787. https://doi.org/10.1177/0040517511410102

    Article  Google Scholar 

  73. Zhang H, Hu J, He Z (2017) Fabric defect detection based on visual saliency map and SVM. 2nd IEEE international conference on computational intelligence and applications (ICCIA), Beijing, pp. 322-326. https://doi.org/10.1109/CIAPP.2017.8167231

  74. Zhang J, Wang J, Pan R, Zhou J, Gao W (2017) A computer vision-based system for automatic detection of misarranged warp yarns in yarn-dyed fabric. Part I: continuous segmentation of warp yarns. J Text Inst 109(5):577–584. https://doi.org/10.1080/00405000.2017.1361580

    Article  Google Scholar 

  75. Zhang K, Yan Y, Li P, Jing J, Liu X, Wang Z (2018) Fabric defect detection using salience metric for color dissimilarity and positional aggregation. IEEE Access 6:49170–49181. https://doi.org/10.1109/ACCESS.2018.2868059

    Article  Google Scholar 

  76. Zhang C, Liu W, Xing W (2018) Color image enhancement based on local spatial homomorphic filtering and gradient domain variance guided image filtering. J Electron Imaging 27(06):01–10. https://doi.org/10.1117/1.jei.27.6.063026

    Article  Google Scholar 

  77. Zhang H, Zhang L, Li P, Gu D (2018) Yarn-dyed fabric defect detection with YOLOV2 based on deep convolution neural networks. IEEE 7th data driven control and learning systems conference (DDCLS), Enshi, pp. 170-174. https://doi.org/10.1109/DDCLS.2018.8516094

  78. Zhou H (2012) An stationary wavelet transform and curvelet transform based infrared and visible images fusion algorithm. Int J Digit Content Technol Appl 6(1):144–151. https://doi.org/10.4156/jdcta.vol6.issue1.18

    Article  Google Scholar 

Download references

Acknowledgments

Authors would like to thank the Department of Science and Technology, Government of India for providing research facilities, and for the active encouragement and support.

Code availability

Not applicable.

Funding

This research received funding from Ministry of Science and Technology, Department of Science and Technology, Government of India, under Grant Agreement F.No.: DST/SSTP/2018/232(G), TPN No. 18521 dated 31 March 2019.

Author information

Authors and Affiliations

  1. Electronics and Communication Engineering, Kongu Engineering College, Perundurai, India

    T. Meeradevi, S. Sasikala, S. Gomathi & K. Prabakaran

Authors
  1. T. Meeradevi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Sasikala
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. Gomathi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. K. Prabakaran
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Not applicable.

Corresponding author

Correspondence to T. Meeradevi.

Ethics declarations

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Conflicts of interest/competing interests

Authors declare no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Meeradevi, T., Sasikala, S., Gomathi, S. et al. An analytical survey of textile fabric defect and shade variation detection system using image processing. Multimed Tools Appl 82, 6167–6196 (2023). https://doi.org/10.1007/s11042-022-13575-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-13575-8

Keywords

Search

Navigation