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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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.
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.
LI Y, ZHANG C. Automated vision system for fabric defect inspection using Gabor filters and PCNN [J]. SpringerPlus, 2016, 5(1): 765.
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.
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.
CHEN X, HUANG X B. Evaluating fabric pilling with light-projected image analysis [J]. Textile Research Journal, 2004, 74(11): 977–981.
KIM S C, KANG T J. Image analysis of standard pilling photographs using wavelet reconstruction [J]. Textile Research Journal, 2005, 75(12): 801–811.
PARK C K, KANG T J. Objective rating of seam pucker using neural networks [J]. Textile Research Journal, 1997, 67(7): 494–502.
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.
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.
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).
SU J, XU B. Fabric wrinkle evaluation using laser triangulation and neural network classifier [J]. Optical Engineering, 1999, 38(10): 1688–1693.
BARI A S M H, GAVRILOVA M L. Artificial neural network based gait recognition using Kinect sensor [J]. IEEE Access, 2019, 7: 162708–162722.
GAO G, WÜTHRICH M V. Convolutional neural network classification of telematics car driving data [J]. Risks, 2019, 7(1): 6.
SERGEEV A, DEL BALSO M. Horovod: Fast and easy distributed deep learning in TensorFlow [DB/OL]. (2018-02-15) [2019-11-25]. https://arxiv.org/abs/1802.05799.
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.
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
- fabric flatness
- convolutional neural network (CNN)
- computer vision
- image processing
- TP 391.4