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Objective Evaluation of Fabric Flatness Grade Based on Convolutional Neural Network

Abstract

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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Authors

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). https://doi.org/10.1007/s12204-020-2239-3

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Key words

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

CLC number

  • TP 391.4

Document code

  • A