The recognition of woven fabric pattern is a crucial task for mass manufacturing and quality control in the textile industry. Traditional methods based on image processing have some limitations on accuracy and stability. In this paper, an automatic method is proposed to jointly realize yarn location and weave pattern recognition. First, a new big fabric dataset is established by a portable wireless device. The dataset contains wide kinds of fabrics and detailed fabric structure parameters. Then, a novel multi-task and multi-scale convolutional neural network (MTMSnet) is proposed to predict the location maps of yarns and floats. By adopting the multi-task structure, the MTMSnet can better learn the related features between yarns and floats. Finally, the weave pattern and basic weave repeat are recognized by combining the yarn and float location maps. Extensive experimental results on various kinds of fabrics indicate that the proposed method achieves high accuracy and quality in weave pattern recognition.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Aldemir, E., Özdemir, H., & Sarı, Z. (2018). An improved gray line profile method to inspect the warp–weft density of fabrics. The Journal of The Textile Institute,104, 1–12.
Boonsirisumpun, N., & Puarungroj, W. (2018). Loei fabric weaving pattern recognition using deep neural network. In 2018 15th International joint conference on computer science and software engineering (JCSSE), 2018 (pp. 1–6). IEEE.
Dai, J., He, K., & Sun, J. (2016). Instance-aware semantic segmentation via multi-task network cascades. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2016 (pp. 3150–3158).
Duda, R. O., & Hart, P. E. (1972). Use of the hough transform to detect lines and curves in pictures. Communications of the ACM,15(1), 11–15.
Glorot, X., & Bengio, Y. (2010). Understanding the difficulty of training deep feedforward neural networks. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics,9, 249–256.
Golik, P., Doetsch, P., & Ney, H. (2013). Cross-entropy vs. squared error training: A theoretical and experimental comparison. In: Interspeech, 2013 (pp. 1756–1760).
Guo, Y., Ge, X., Yu, M., Yan, G., & Liu, Y. (2019). Automatic recognition method for the repeat size of a weave pattern on a woven fabric image. Textile Research Journal,89(14), 2754–2775.
Huang, C.-C., Liu, S.-C., & Yu, W.-H. (2000). Woven fabric analysis by image processing: Part I: Identification of weave patterns. Textile Research Journal,70(6), 481–485.
Jing, J., Xu, M., Li, P., Qi, L., & Liu, S. (2014). Automatic classification of woven fabric structure based on texture feature and PNN. Fibers and Polymers,15(5), 1092–1098.
Kang, T. J., Kim, C. H., & Oh, K. W. (1999). Automatic recognition of fabric weave patterns by digital image analysis. Textile Research Journal,69(2), 77–83.
Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:14091556.
Kinoshita, M., Hashimoto, Y., Akiyama, R., & Uchiyama, S. (1989). Determination of weave type in woven fabric by digital image processing. Journal of the Textile Machinery Society of Japan,35(2), 1–4.
Kuo, C.-F. J., Shih, C.-Y., Huang, C.-C., Su, T.-L., & Liao, I.-C. (2016). A novel image processing technology for recognizing the weave of fabrics. Textile Research Journal,86(3), 288–301.
Lachkar, A., Benslimane, R., D’orazio, L., & Martuscelli, E. (2005). Textile woven fabric recognition using Fourier image analysis techniques: Part II–texture analysis for crossed-states detection. Journal of the Textile Institute,96(3), 179–183.
Li, Z., Meng, S., Wang, L., Zhang, N., & Gao, W. (2019). Intelligent recognition of the patterns of yarn-dyed fabric based on LSRT images. Journal of Engineered Fibers and Fabrics,14, 1558925019840659.
Li, P. F., Wang, J., Zhang, H. H., & Jing, J. F. (2013). Automatic woven fabric classification based on support vector machine. In International conference on automatic control and artificial intelligence, 2013 (pp. 581–584).
Lin, H., Li, B., Wang, X., Shu, Y., & Niu, S. (2019). Automated defect inspection of LED chip using deep convolutional neural network. Journal of Intelligent Manufacturing,30(6), 2525–2534.
Malaca, P., Rocha, L. F., Gomes, D., Silva, J., & Veiga, G. (2019). Online inspection system based on machine learning techniques: real case study of fabric textures classification for the automotive industry. Journal of Intelligent Manufacturing,30(1), 351–361.
Meng, S., Pan, R., Gao, W., Zhou, J., Wang, J., & He, W. (2019). Woven fabric density measurement by using multi-scale convolutional neural networks. IEEE Access,7, 75810–75821.
Ohtsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems Man and Cybernetics,9(1), 62–66.
Pan, R., Gao, W., Liu, J., & Wang, H. (2010a). Automatic recognition of woven fabric patterns based on pattern database. Fibers and Polymers,11(2), 303–308.
Pan, R., Gao, W., Liu, J., & Wang, H. (2010b). Automatic detection of the layout of color yarns for yarn-dyed fabric via a FCM algorithm. Textile Research Journal,80(12), 1222–1231.
Pan, R., Gao, W., Liu, J., & Wang, H. (2011). Automatic recognition of woven fabric pattern based on image processing and BP neural network. Journal of the Textile Institute Proceedings and Abstracts,102(1), 19–30.
Pan, R., Gao, W., Liu, J., Wang, H., & Zhang, X. (2010c). Automatic detection of structure parameters of yarn-dyed fabric. Textile Research Journal,80(17), 1819–1832.
Sabuncu, M., & Ã-zdemir, H. (2015). Recognition of fabric weave patterns using optical coherence tomography. Journal of the Textile Institute Proceedings and Abstracts,107(11), 1406–1411.
Schneider, D., Gloy, Y. S., & Merhof, D. (2015). Vision-based on-loom measurement of yarn densities in woven fabrics. IEEE Transactions on Instrumentation and Measurement,64(4), 1063–1074.
Schneider, D., & Merhof, D. (2015). Blind weave detection for woven fabrics. Pattern Analysis and Applications,18(3), 725–737.
Shen, J., Zou, X., Xu, F., & Xian, Z. (2010). Intelligent recognition of fabric weave patterns using texture orientation features. In International conference on information computing and applications, 2010 (pp. 8–15). Berlin: Springer.
Sindagi, V. A., & Patel, V. M. (2017). CNN-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In 2017 14th IEEE international conference on advanced video and signal based surveillance (AVSS), 2017.
Tabernik, D., Šela, S., Skvarč, J., & Skočaj, D. (2020). Segmentation-based deep-learning approach for surface-defect detection. Journal of Intelligent Manufacturing,31(3), 759–776. https://doi.org/10.1007/s10845-019-01476-x.
Wang, X., Georganas, N. D., & Petriu, E. M. (2010a). Fabric texture analysis using computer vision techniques. IEEE Transactions on Instrumentation and Measurement,60(1), 44–56.
Wang, X., Georganas, N. D., & Petriu, E. M. (2010). Automatic woven fabric structure identification by using principal component analysis and fuzzy clustering. In 2010 IEEE instrumentation and measurement technology conference proceedings, 2010 (pp. 590–595). IEEE.
Xiao, Z., Guo, Y., Geng, L., Wu, J., Zhang, F., Wang, W., et al. (2018a). Automatic recognition of woven fabric pattern based on TILT. Mathematical Problems in Engineering,2018, 1–12.
Xiao, Z., Liu, X., Wu, J., Geng, L., Sun, Y., Zhang, F., et al. (2018b). Knitted fabric structure recognition based on deep learning. The Journal of The Textile Institute,109(9), 1217–1223.
Xin, B., Hu, J., Baciu, G., & Yu, X. (2009). Investigation on the classification of weave pattern based on an active grid model. Textile Research Journal,79(12), 1123–1134.
Xu, B. (1996). Identifying fabric structures with fast Fourier transform techniques. Textile Research Journal,66(8), 496–506.
Zhang, T. Y., & Suen, C. Y. (1984). A fast parallel algorithm for thinning digital patterns. Communications of the ACM,27(3), 236–239.
Zhang, K., Zhang, Z., Li, Z., & Yu, Q. (2016). Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Processing Letters,23(10), 1499–1503.
The authors are thankful to the National Natural Science Foundation of China under Grant 61976105, for providing financial support for this research work.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Meng, S., Pan, R., Gao, W. et al. A multi-task and multi-scale convolutional neural network for automatic recognition of woven fabric pattern. J Intell Manuf (2020). https://doi.org/10.1007/s10845-020-01607-9
- Weave pattern recognition
- Texture analysis
- Computer vision
- Multi-task learning
- Convolutional neural network