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Objective Evaluation of Fabric Flatness Grade Based on Convolutional Neural Network


As an important indicator for the appearance and intrinsic quality of textiles, fabric flatness is the immediate cause affecting the aesthetic appearance and performance of textiles. In this paper, the objective evaluation system of fabric flatness based on 3D scanner and convolutional neural network (CNN) is constructed by using the height data of AATCC flatness template. The 3D scanner is responsible for the collection of the height value data of the sample. The effect of different sub-sample cutting sizes, cutting offsets, and network model depths on the objective evaluation coincidence rate of multiple flatness level was studied. The experimental results show that the coincidence rate of the system reaches 98.9% when the collected sample data are cut into subsamples of 20 pixel × 20 pixel with 12 pixel cutting offsets and the 11-layer network model is selected. Finally, this scheme is used to evaluate the flatness of four real fabrics with different colors and textures. The result shows that all of the samples can achieve a higher coincidence rate, which further verifies the adaptability and stability of the objective evaluation system constructed in this paper for fabric flatness evaluation.

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  1. [1]

    WANG R W, WU X Y, WANG S Y, et al. Automatic identification of ramie and cotton fibers using characteristics in longitudinal view, Part I: Locating capture of fiber images [J]. Textile Research Journal, 2009, 79(14): 1251–1259.

    Article  Google Scholar 

  2. [2]

    STIVANELLO M E, VARGAS S, ROLOFF M L, et al. Automatic detection and classification of defects in knitted fabrics [J]. IEEE Latin America Transactions, 2016, 14(7): 3065–3073.

    Article  Google Scholar 

  3. [3]

    LI Y, ZHANG C. Automated vision system for fabric defect inspection using Gabor filters and PCNN [J]. SpringerPlus, 2016, 5(1): 765.

    Article  Google Scholar 

  4. [4]

    YANG W, LI D, ZHU L, et al. A new approach for image processing in foreign fiber detection [J]. Computers and Electronics in Agriculture, 2009, 68(1): 68–77.

    Article  Google Scholar 

  5. [5]

    SU Z, TIAN G Y, GAO C. A machine vision system for on-line removal of contaminants in wool [J]. Mechatronics, 2006, 16(5): 243–247.

    Article  Google Scholar 

  6. [6]

    CHEN X, HUANG X B. Evaluating fabric pilling with light-projected image analysis [J]. Textile Research Journal, 2004, 74(11): 977–981.

    Article  Google Scholar 

  7. [7]

    KIM S C, KANG T J. Image analysis of standard pilling photographs using wavelet reconstruction [J]. Textile Research Journal, 2005, 75(12): 801–811.

    Article  Google Scholar 

  8. [8]

    PARK C K, KANG T J. Objective rating of seam pucker using neural networks [J]. Textile Research Journal, 1997, 67(7): 494–502.

    Article  Google Scholar 

  9. [9]

    PARK C K, KANG T J. Objective evaluation of seam pucker using artificial intelligence, Part I: Geometric modeling of seam pucker [J]. Textile Research Journal, 1999, 69(10): 735–742.

    Article  Google Scholar 

  10. [10]

    LIU C. Investigation on the novel measurement for fabric wrinkle simulating actual wear [J]. The Journal of The Textile Institute, 2017, 108(2): 279–286.

    Google Scholar 

  11. [11]

    ZHANG N, PAN R R, GAO W D. Automatic seam-puckering evaluation using image processing [J]. Journal of Textile Research, 2017, 38(4): 145–150 (in Chinese).

    Google Scholar 

  12. [12]

    SU J, XU B. Fabric wrinkle evaluation using laser triangulation and neural network classifier [J]. Optical Engineering, 1999, 38(10): 1688–1693.

    Article  Google Scholar 

  13. [13]

    BARI A S M H, GAVRILOVA M L. Artificial neural network based gait recognition using Kinect sensor [J]. IEEE Access, 2019, 7: 162708–162722.

    Article  Google Scholar 

  14. [14]

    GAO G, WÜTHRICH M V. Convolutional neural network classification of telematics car driving data [J]. Risks, 2019, 7(1): 6.

    Article  Google Scholar 

  15. [15]

    SERGEEV A, DEL BALSO M. Horovod: Fast and easy distributed deep learning in TensorFlow [DB/OL]. (2018-02-15) [2019-11-25].

  16. [16]

    ABRIL H C, MILLÁN M S, VALENCIA E. Influence of the wrinkle perception with distance in the objective evaluation of fabric smoothness [J]. Journal of Optics A: Pure and Applied Optics, 2008, 10(10): 104030.

    Article  Google Scholar 

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Corresponding author

Correspondence to Jun Wang.

Additional information

Foundation item: the Fundamental Research Funds for the Central Universities (No. CUSF-DF-D-2018039)

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Zhan, Z., Zhang, W., Chen, X. et al. Objective Evaluation of Fabric Flatness Grade Based on Convolutional Neural Network. J. Shanghai Jiaotong Univ. (Sci.) 26, 503–510 (2021).

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Key words

  • fabric flatness
  • convolutional neural network (CNN)
  • computer vision
  • image processing

CLC number

  • TP 391.4

Document code

  • A