Rolling Surface Defect Inspection for Drum-Shaped Rollers Based on Deep Learning

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

Rolling Surface Defect Inspection for Drum-Shaped Rollers Based on Deep Learning

Tao, J Zhu, Y Jiang, F

Liu, H Liu, H

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Sensors Journal, 2022, 22, (9), pp. 8693-8700
Issue Date:: 2022-05-01

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Field	Value	Language
dc.contributor.author	Tao, J
dc.contributor.author	Zhu, Y
dc.contributor.author	Jiang, F https://orcid.org/0000-0003-3765-561X
dc.contributor.author	Liu, H
dc.contributor.author	Liu, H
dc.date.accessioned	2023-07-09T01:23:28Z
dc.date.available	2023-07-09T01:23:28Z
dc.date.issued	2022-05-01
dc.identifier.citation	IEEE Sensors Journal, 2022, 22, (9), pp. 8693-8700
dc.identifier.issn	1530-437X
dc.identifier.issn	1558-1748
dc.identifier.uri	http://hdl.handle.net/10453/171355
dc.description.abstract	It is difficult to detect defects such as shallow dents and rust on rolling surfaces by using the traditional vision-based surface inspection method (line light source and line array camera mode) which has a low sensitivity to these defects. This paper presents a method that introduces the fringe projection technique for traditional visual inspection devices to overcome the limitations of the traditional methods and uses deep-learning techniques for detecting defects such as cuts, abrasions, dents, and rust on the rolling surfaces of drum-shaped rollers. A new artificial-intelligence-based labeling method, namely, the Padua Incremental Mask Labeling Method, has been introduced for accelerating the calibration process used for defect detection, and based on a one-stage architecture, the You-Only-Look-Once-OurNet (YOLO-OurNet) deep-learning network has been designed for detecting the defects on the rolling surfaces of drum-shaped rollers. From the results of the experimental tests, the time required for detecting a defect has been found to be 0.024s, an accuracy rate of up to 99.2%, and the value of object detection evaluation index F1 of up to 0.988. Our method outperforms the related method on the domain of rolling surface defect detection.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Sensors Journal
dc.relation.isbasedon	10.1109/JSEN.2022.3159743
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.subject.classification	40 Engineering
dc.title	Rolling Surface Defect Inspection for Drum-Shaped Rollers Based on Deep Learning
dc.type	Journal Article
utslib.citation.volume	22
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	2023-07-09T01:23:24Z
pubs.issue	9
pubs.publication-status	Published
pubs.volume	22
utslib.citation.issue	9

Abstract:

It is difficult to detect defects such as shallow dents and rust on rolling surfaces by using the traditional vision-based surface inspection method (line light source and line array camera mode) which has a low sensitivity to these defects. This paper presents a method that introduces the fringe projection technique for traditional visual inspection devices to overcome the limitations of the traditional methods and uses deep-learning techniques for detecting defects such as cuts, abrasions, dents, and rust on the rolling surfaces of drum-shaped rollers. A new artificial-intelligence-based labeling method, namely, the Padua Incremental Mask Labeling Method, has been introduced for accelerating the calibration process used for defect detection, and based on a one-stage architecture, the You-Only-Look-Once-OurNet (YOLO-OurNet) deep-learning network has been designed for detecting the defects on the rolling surfaces of drum-shaped rollers. From the results of the experimental tests, the time required for detecting a defect has been found to be 0.024s, an accuracy rate of up to 99.2%, and the value of object detection evaluation index F1 of up to 0.988. Our method outperforms the related method on the domain of rolling surface defect detection.

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http://hdl.handle.net/10453/171355