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Multi-Layer Feature Extraction with Deformable Convolution for Fabric Defect Detection

Jielin Jiang1,2,3,4,*, Chao Cui1, Xiaolong Xu1,2,3,4, Yan Cui5

1 School of Computer Science, Nanjing University of Information Science and Technology, Nanjing, 210044, China
2 State Key Lab. for Novel Software Technology, Nanjing University, Nanjing, 210023, China
3 Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, 210044, China
4 Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, 210044, China
5 College of Mathematics and Information Science, Nanjing Normal University of Special Education, Nanjing, 210038, China

* Corresponding Author: Jielin Jiang. Email: email

Intelligent Automation & Soft Computing 2024, 39(4), 725-744. https://doi.org/10.32604/iasc.2024.036897

Abstract

In the textile industry, the presence of defects on the surface of fabric is an essential factor in determining fabric quality. Therefore, identifying fabric defects forms a crucial part of the fabric production process. Traditional fabric defect detection algorithms can only detect specific materials and specific fabric defect types; in addition, their detection efficiency is low, and their detection results are relatively poor. Deep learning-based methods have many advantages in the field of fabric defect detection, however, such methods are less effective in identifying multi-scale fabric defects and defects with complex shapes. Therefore, we propose an effective algorithm, namely multi-layer feature extraction combined with deformable convolution (MFDC), for fabric defect detection. In MFDC, multi-layer feature extraction is used to fuse the underlying location features with high-level classification features through a horizontally connected top-down architecture to improve the detection of multi-scale fabric defects. On this basis, a deformable convolution is added to solve the problem of the algorithm’s weak detection ability of irregularly shaped fabric defects. In this approach, Roi Align and Cascade-RCNN are integrated to enhance the adaptability of the algorithm in materials with complex patterned backgrounds. The experimental results show that the MFDC algorithm can achieve good detection results for both multi-scale fabric defects and defects with complex shapes, at the expense of a small increase in detection time.

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APA Style
Jiang, J., Cui, C., Xu, X., Cui, Y. (2024). Multi-layer feature extraction with deformable convolution for fabric defect detection. Intelligent Automation & Soft Computing, 39(4), 725-744. https://doi.org/10.32604/iasc.2024.036897
Vancouver Style
Jiang J, Cui C, Xu X, Cui Y. Multi-layer feature extraction with deformable convolution for fabric defect detection. Intell Automat Soft Comput . 2024;39(4):725-744 https://doi.org/10.32604/iasc.2024.036897
IEEE Style
J. Jiang, C. Cui, X. Xu, and Y. Cui, “Multi-Layer Feature Extraction with Deformable Convolution for Fabric Defect Detection,” Intell. Automat. Soft Comput. , vol. 39, no. 4, pp. 725-744, 2024. https://doi.org/10.32604/iasc.2024.036897



cc Copyright © 2024 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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