A multi-task and multi-scale convolutional neural network for automatic recognition of woven fabric pattern

Abstract

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.

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Acknowledgements

The authors are thankful to the National Natural Science Foundation of China under Grant 61976105, for providing financial support for this research work.

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Correspondence to Ruru Pan.

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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

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Keywords

  • Weave pattern recognition
  • Texture analysis
  • Computer vision
  • Multi-task learning
  • Convolutional neural network