Fabric defect detection based on multi-source feature fusion
International Journal of Clothing Science and Technology
ISSN: 0955-6222
Article publication date: 21 June 2021
Issue publication date: 11 March 2022
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
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