University of Oulu

D. Shi et al., "Machine Vision-Based Segmentation and Classification Method for Intelligent Roller Surface Monitoring," 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), Guangzhou, 2018, pp. 1811-1817,

Machine vision-based segmentation and classification method for intelligent roller surface monitoring

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Author: Shi, Depeng1; Zhou, Jiehan2; Xu, Jirui3;
Organizations: 1Department of MEE of Southern University of Science and Technology. China
2Information Technology and Electrical Engineering in Oulu University. Finland
3Department of Mechanical Engineering, Tsinghua University, China
4epartment of Mechanical Engineering, Tsinghua university, China
5Department of Mechanical Engineering and the State Key Lab of Tribology, Tsinghua University. China
6HIECISE Precision Equipment Co. Ltd. China
7Mechanical and Energy Engineering, Southern University of Science and Technology, China
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.6 MB)
Persistent link:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2018
Publish Date: 2020-04-29


The surface quality of steel rollers is a key factor determining the quality of final products such as metal sheets and foils in the rolling industry. It is important to examine the surface quality of rollers since rollers with optical defects will always duplicate the defects onto the metal sheet or foil during rolling. The typical optical defects of rollers after finish grinding include speckles, chatter marks, swirl marks and combination of all of the above. They can hardly be modeled or shaped by the approach of micro topography or SEM (scanning electrical microscope). In this paper, an on-site machine vision system is firstly applied for stable inspection for the optical defects on roller surfaces. Then, an improved optical defect segmentation algorithm is developed based on the active contour model and the images including chatter marks and swirl marks. The normal surface state is classified by the combination of methods of Gabor filters, KPCA method and ELM neural networks. Finally, experiment are carried out to verify the efficiency of the improved segmentation method and the recognition rate of the combined classification algorithm.

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ISBN: 978-1-5386-9380-3
ISBN Print: 978-1-5386-9381-0
Pages: 1811 - 1817
Article number: 8560283
DOI: 10.1109/SmartWorld.2018.00305
Host publication: 4th IEEE SmartWorld, 15th IEEE International Conference on Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovations, SmartWorld/UIC/ATC/ScalCom/CBDCom/IoP/SCI 2018
Host publication editor: Loulergue, F.
Wang, G.
Bhuiyan, M. Z. A.
Ma, X.
Li, P.
Roveri, M.
Han, Q.
Chen, L.
Conference: IEEE International Conference on Cloud and Big Data Computing
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: Resrach supported by Tsinghua University Initiative Scientific Research Program Tsinghua-RWTH Aachen Collaborative Innovation and Southern University of Science and Technology.
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