Skip to main content
SpringerLink
Log in

Novel proposed technique for automatic fabric defect detection

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

Abstract

Texture analysis plays an important role in many image processing applications to describe the objects. On the other hand, visual Fabric Defect Detection (FDD) is a highly research field in the computer vision. Surface defect refers to abnormalities in the texture of the surface. So, in this paper a dual purpose benchmark dataset is proposed for texture image analysis and surface defect detection. The first framework is based Segmentation with Contrast Limited Histogram Equalization (CLAHE) and finally FE for Classification (SCFC). The SCFC depends on improvement using CLAHE in addition to pre-processing followed by segmentation by OT and finally FE for classification task. The second scheme is relied on merging the features of A Trous Algorithm with Homomorphic Method (HM) (AH) following by Segmentation and Feature Extraction (FE) for Classification (AHSFC). The AHSFC depends on enhancement using AH in addition to pre-processing followed by segmentation using Optimum Global Thresholding (OT) and finally FE for the detection or classification task. The performance quality metrics for the suggested techniques are entropy, average gradient, contrast, Sobel edge magnitude, sensitivity, specificity, precision, accuracy and identification time.Simulation results prove that the success of both techniques in detecting the FDD. By comparing the first and the second presented algorithms, it is clear that the second suggested technique gives superior for the FDD the clothing.

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

Similar content being viewed by others

Data availability

All data generated or analyzed during this study are included in this published article (and its supplementary information files).

References

  1. Ashiba HI, Awadallah KH, El-Halfawy SM, El-Samie FEA (2008) Homomorphic enhancement of infrared images using the additive wavelet transform. Prog Electromag Res C 1:123–130

    Article  Google Scholar 

  2. Ashiba HI, Mansour HM, Ahmed HM, El-Kordy MF, Dessouky MI, El-Samie FEA (2018) Enhancement of Infrared images based on efficient histogram processing. Wirel Pers Commun 99:619–636

    Article  Google Scholar 

  3. Ashiba HI, Mansour HM, Ahmed HM, El-Kordy MF, Dessouky MI, Zahran O, El-Samie FEA (May 2019) Enhancement of IR images using histogram processing and the Undecimated additive wavelet transform. Multimed Tools Appl 78(9):11277–11290

    Article  Google Scholar 

  4. Brzozowskia K, Frankowskab E, Piaseckia P, Zięcina P, Zukowski P, Walecka RB (2011) The use of routine imaging data in diagnosis of cerebral pseudoaneurysm prior to Angiography. Eur J Radiol 80:e401–e409

    Article  Google Scholar 

  5. Fan J, Yang J, Wang Y, Yang S, Ai D, Huang Y, Song H, Wang Y, Shen D (2019) Deep feature descriptor based hierarchical dense matching for X-ray angiographic images. Comput Methods Prog Biomed 175:233–242

    Article  Google Scholar 

  6. Gonzalez RC, Woods RE (2008) Digital ImageProcessing

  7. Jin R, Niu Q (2021) "Automatic Fabric Defect Detection Based on an Improved YOLOv5", Hindawi ,Mathematical Problems in Engineering , Article ID 7321394, pp.1–13 https://doi.org/10.1155/2021/7321394

  8. Jin S, Fan D, Malekian R, Duan Z, Li Z (2018) An image recognition method for gear fault diagnosis in the manufacturing line of short filament fibres. Insight-Non-Destruct Test Cond Moni 60(5):270–275

    Article  Google Scholar 

  9. Jin S, Lin Q, Bie Y, Ma Q, Li Z (2020) A practical method for detecting fluff quality of fabric surface using optimal sensing. Elektronika IR Elektrotechnika 26(1):58–62

    Article  Google Scholar 

  10. Jin S, Chen Y, Yin J, Li Y, Gupta MK, Fracz P, Li Z (2020) Three-Dimensional Reconstruction of Fleece Fabric Surface for Thickness Evaluation. Electronics 9(9):1–12

    Article  Google Scholar 

  11. Jing J, Zhuo D, Zhang H (2020) “Fabric defect detection using the improved YOLOv3 model,” Journal of Engineered Fibers and Fabrics, vol. 15

  12. Kumbhar U, Patil V, Rudrakshi S (2013) Enhancement Of Medical Images Using Image Processing In Matlab. Int J Engin Res Technol 2(4):2278–0181

    Google Scholar 

  13. Kuznetsova A, Maleva T, Soloviev V (2020) “Detecting apples in orchards using YOLOv3 and YOLOv5 in general and close-up images,” in Proceedings of the International Symposium on Neural Networks, Cairo, Egypt.

  14. Latha R, Senthil Kumar S, Manohar V (2010) Segmentation of linear structures from medical images. Proc Comp Sci 2:303–306

    Article  Google Scholar 

  15. Lim JS (1990) Two -Dimensional signal and image processing. Prentice Hall Inc

    Google Scholar 

  16. MATLAB (2010) Version 7.10.0. Natick, Massachusettse: The MathWorks Inc.

  17. Rajalingam B, Priya R (2017) Multimodality Medical Image Fusion Based on Hybrid Fusion Techniques. Int J Engin Manufact Sci 7(1):2249–3115

    Google Scholar 

  18. Rajalingam B, Priya R (2017) A novel approach for multimodal medical image fusion using hybrid fusion algorithms for disease analysis. Int J Pure App Mathem 117(15):599–619

    Google Scholar 

  19. Singh BB, Patel S (2017) Efficient Medical Image Enhancement using CLAHE Enhancement and Wavelet Fusion. Int J Comput Appl 167(5):0975–8887

    Google Scholar 

  20. Wang X, Liu P, Wang F (2010) Fabric-skin friction property measurement system. Int J Clothing Sci Technol 22(4):285–296

    Article  Google Scholar 

  21. P. Wildner, M. Stasiołek, M. Matysiak (2020) " Differential diagnosis of multiple sclerosis and other inflammatory CNS Diseases", Multiple Sclerosis and Related Disorders vol. 37, No. 101452

  22. Wu Z, Zhuo Y, Li J, Feng Y, Han B, Liao S (2018) A Fast monochromatic fabric defect Fast detection method based on convolutional neural network. J Comp-Aided Des Comp Grap 30(12):2262

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Electrical Communications Engineering, Bilbis Higher Institute of Engineering, Bilbis, Sharqia, Egypt

    Huda I. Ashiba

  2. Department of Basic Sciences, Bilbis Higher Institute of Engineering, Bilbis, Sharqia, Egypt

    Mabrouka I. Ashiba

Authors
  1. Huda I. Ashiba
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mabrouka I. Ashiba
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mabrouka I. Ashiba.

Ethics declarations

Conflict of interest

The authors declare that they have 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 (e.g. a society or other partner) 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

Ashiba, H.I., Ashiba, M.I. Novel proposed technique for automatic fabric defect detection. Multimed Tools Appl 82, 30783–30806 (2023). https://doi.org/10.1007/s11042-023-14368-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-14368-3

Keywords

Search

Navigation