A Novel Fabric Defect Detection Network Based on Attention Mechanism and Multi-Task Fusion

Peng, Z; Gong, X; Lu, Z; Xu, X; Wei, B; Prasad, M

A Novel Fabric Defect Detection Network Based on Attention Mechanism and Multi-Task Fusion

Peng, Z Gong, X Lu, Z Xu, X Wei, B Prasad, M

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings of 2021 7th IEEE International Conference on Network Intelligence and Digital Content, IC-NIDC 2021, 2022, 00, pp. 484-488
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Peng, Z
dc.contributor.author	Gong, X
dc.contributor.author	Lu, Z
dc.contributor.author	Xu, X
dc.contributor.author	Wei, B
dc.contributor.author	Prasad, M https://orcid.org/0000-0002-7745-9667
dc.date	2021-11-17
dc.date.accessioned	2023-03-24T10:42:52Z
dc.date.available	2023-03-24T10:42:52Z
dc.date.issued	2022-01-01
dc.identifier.citation	Proceedings of 2021 7th IEEE International Conference on Network Intelligence and Digital Content, IC-NIDC 2021, 2022, 00, pp. 484-488
dc.identifier.isbn	9781665405829
dc.identifier.issn	2374-0272
dc.identifier.issn	2575-4955
dc.identifier.uri	http://hdl.handle.net/10453/168376
dc.description.abstract	Fabric is an important material, which is applied in the entire process of textile manufacturing, such as spinning, weaving, dyeing, printing, and finishing, and garments manufacturing. As defects on the surface of the fabric are inevitable in the process of fabric production, the defect detection of fabric is significant for fabric manufacture. The current CNN-based defect detection methods face several challenges when tackling the fabric defect with a tiny shape, the low grayscale difference with background, and ambiguous defect type. To deal with the problem, we proposed a novel fabric defect detection network - AMTFNet based on the attention mechanism and multi-task fusion module in this paper. On one hand, the attention mechanism module forced networks to pay attention to defects. On the other hand, the multi-task fusion module helps AMTFNet to further improve the classification effect using feature concatenated. The experimental result indicates that the precision-score, recall-score, and F1-score of AMTFNet reach 0.980, 0.994, and 0.987, respectively. The proposed method can be successfully applied in the detection of industrial fabric material.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings of 2021 7th IEEE International Conference on Network Intelligence and Digital Content, IC-NIDC 2021
dc.relation.ispartof	IEEE International Conference on Network Intelligence and Digital Content
dc.relation.isbasedon	10.1109/IC-NIDC54101.2021.9660399
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A Novel Fabric Defect Detection Network Based on Attention Mechanism and Multi-Task Fusion
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Beijing, China
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-03-24T10:42:51Z
pubs.finish-date	2021-11-19
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2021-11-17
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

Fabric is an important material, which is applied in the entire process of textile manufacturing, such as spinning, weaving, dyeing, printing, and finishing, and garments manufacturing. As defects on the surface of the fabric are inevitable in the process of fabric production, the defect detection of fabric is significant for fabric manufacture. The current CNN-based defect detection methods face several challenges when tackling the fabric defect with a tiny shape, the low grayscale difference with background, and ambiguous defect type. To deal with the problem, we proposed a novel fabric defect detection network - AMTFNet based on the attention mechanism and multi-task fusion module in this paper. On one hand, the attention mechanism module forced networks to pay attention to defects. On the other hand, the multi-task fusion module helps AMTFNet to further improve the classification effect using feature concatenated. The experimental result indicates that the precision-score, recall-score, and F1-score of AMTFNet reach 0.980, 0.994, and 0.987, respectively. The proposed method can be successfully applied in the detection of industrial fabric material.

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http://hdl.handle.net/10453/168376