Smooth Surface Defect Detection by Deep Learning Based on Wrapped Phase Map

Tao, J; Zhu, Y; Liu, W; Jiang, F; Liu, H

Smooth Surface Defect Detection by Deep Learning Based on Wrapped Phase Map

Tao, J Zhu, Y Liu, W Jiang, F

Liu, H

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Sensors Journal, 2021, 21, (14), pp. 16236-16244
Issue Date:: 2021-07-15

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Field	Value	Language
dc.contributor.author	Tao, J
dc.contributor.author	Zhu, Y
dc.contributor.author	Liu, W
dc.contributor.author	Jiang, F https://orcid.org/0000-0003-3765-561X
dc.contributor.author	Liu, H
dc.date.accessioned	2022-01-04T05:56:20Z
dc.date.available	2022-01-04T05:56:20Z
dc.date.issued	2021-07-15
dc.identifier.citation	IEEE Sensors Journal, 2021, 21, (14), pp. 16236-16244
dc.identifier.issn	1530-437X
dc.identifier.issn	1558-1748
dc.identifier.uri	http://hdl.handle.net/10453/152667
dc.description.abstract	Object detection is widely used in the industry for visual inspection of defects. But it fails to fully reflect the defects on the smooth surface due to the difficulty in illuminating, and the high performance of defect detection tends to be a challenge. In this paper, we proposed a novel defect detection method for laptop panels. On the one hand, we utilized the wrapped phase map obtained by phase-shift reflection fringes for defect detection and used the DenseFuse network to fuse the wrapped phase map with the fringe modulation map to optimize the dataset. On the other hand, we proposed an improved deep learning-based network Vovecwnet. The experiments indicated that our image-based multi-information fusion system could achieve excellent performance in defect detection on the smooth surface (e.g., laptop panels surface), with high accuracy of 97.5% and a few reference time 0.0762s on GTX 2080ti GPU. Thus, our proposed method outperformed and tackled the challenges encountered in the defect detection of smooth surfaces.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Sensors Journal
dc.relation.isbasedon	10.1109/JSEN.2021.3076610
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0205 Optical Physics, 0906 Electrical and Electronic Engineering, 0913 Mechanical Engineering
dc.subject.classification	Analytical Chemistry
dc.title	Smooth Surface Defect Detection by Deep Learning Based on Wrapped Phase Map
dc.type	Journal Article
utslib.citation.volume	21
utslib.for	0205 Optical Physics
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Provost
pubs.organisational-group	/University of Technology Sydney/Provost/Jumbunna
utslib.copyright.status	closed_access	*
dc.date.updated	2022-01-04T05:56:15Z
pubs.issue	14
pubs.publication-status	Published
pubs.volume	21
utslib.citation.issue	14

Abstract:

Object detection is widely used in the industry for visual inspection of defects. But it fails to fully reflect the defects on the smooth surface due to the difficulty in illuminating, and the high performance of defect detection tends to be a challenge. In this paper, we proposed a novel defect detection method for laptop panels. On the one hand, we utilized the wrapped phase map obtained by phase-shift reflection fringes for defect detection and used the DenseFuse network to fuse the wrapped phase map with the fringe modulation map to optimize the dataset. On the other hand, we proposed an improved deep learning-based network Vovecwnet. The experiments indicated that our image-based multi-information fusion system could achieve excellent performance in defect detection on the smooth surface (e.g., laptop panels surface), with high accuracy of 97.5% and a few reference time 0.0762s on GTX 2080ti GPU. Thus, our proposed method outperformed and tackled the challenges encountered in the defect detection of smooth surfaces.

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http://hdl.handle.net/10453/152667