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Fabric defect detection based on multi-source feature fusion

Zhoufeng Liu (Zhongyuan University of Technology, Zhengzhou, China)
Shanliang Liu (Zhongyuan University of Technology, Zhengzhou, China)
Chunlei Li (Zhongyuan University of Technology, Zhengzhou, China)
Bicao Li (Zhongyuan University of Technology, Zhengzhou, China)

International Journal of Clothing Science and Technology

ISSN: 0955-6222

Article publication date: 21 June 2021

Issue publication date: 11 March 2022

256

Abstract

Purpose

This paper aims to propose a new method to solve the two problems in fabric defect detection. Current state-of-the-art industrial products defect detectors are deep learning-based, which incurs some additional problems: (1) The model is difficult to train due to too few fabric datasets for the difficulty of collecting pictures; (2) The detection accuracy of existing methods is insufficient to implement in the industrial field. This study intends to propose a new method which can be applied to fabric defect detection in the industrial field.

Design/methodology/approach

To cope with exist fabric defect detection problems, the article proposes a novel fabric defect detection method based on multi-source feature fusion. In the training process, both layer features and source model information are fused to enhance robustness and accuracy. Additionally, a novel training model called multi-source feature fusion (MSFF) is proposed to tackle the limited samples and demand to obtain fleet and precise quantification automatically.

Findings

The paper provides a novel fabric defect detection method, experimental results demonstrate that the proposed method achieves an AP of 93.9 and 98.8% when applied to the TILDA(a public dataset) and ZYFD datasets (a real-shot dataset), respectively, and outperforms 5.9% than fine-tuned SSD (single shot multi-box detector).

Research limitations/implications

Our proposed algorithm can provide a promising tool for fabric defect detection.

Practical implications

The paper includes implications for the development of a powerful brand image, the development of “brand ambassadors” and for managing the balance between stability and change.

Social implications

This work provides technical support for real-time detection on industrial sites, advances the process of intelligent manual detection of fabric defects and provides a technical reference for object detection on other industrial

Originality/value

Therefore, our proposed algorithm can provide a promising tool for fabric defect detection.

Keywords

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61772576 and No. 61379113), the Key Natural Science Foundation of Henan Province (No. 162300410338), the Science and Technology Innovation Talent Project of the Education Department of Henan Province (17HASTIT019), and the Henan Science Fund for Distinguished Young Scholars (184100510002).

Citation

Liu, Z., Liu, S., Li, C. and Li, B. (2022), "Fabric defect detection based on multi-source feature fusion", International Journal of Clothing Science and Technology, Vol. 34 No. 2, pp. 156-177. https://doi.org/10.1108/IJCST-07-2020-0108

Publisher

:

Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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