Machine Vision-based Segmentation and Classification Method for Intelligent Roller Surface Monitoring

Shi, Depeng1; Zhou, Jiehan2; Xu, Jirui3; Yang, Jun3; Li, Xuekun3,4; Zhao, Zeming5; Chen, Junchuang5; Rong, Yiming6

10.1109/SmartWorld.2018.00305

2018

978-1-5386-9381-0

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)

1811-1817

8-12 Oct. 2018

Guangzhou, China

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

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.

optical defect detection machine vision big data roller surface monitoring

第一

英语

CPCI-S ; EI

[2017ZX04007001]

Computer Science ; Engineering

Computer Science, Theory & Methods ; Engineering, Electrical & Electronic

WOS:000458742900269

20190406418362

Big data ; Gabor filters ; Image enhancement ; Image segmentation ; Rollers (machine components) ; Rolling ; Sheet metal ; Smart city ; Surface properties ; Trusted computing ; Ubiquitous computing

Machine Components:601.2 ; Electric Filters:703.2 ; Data Processing and Image Processing:723.2 ; Computer Applications:723.5 ; Materials Science:951

Web of Science

1.Southern Univ Sci & Technol, Dept MEE, Shenzhen, Peoples R China
2.Oulu Univ, Fac Informat Technol & Elect Engn, Oulu, Finland
3.Tsinghua Univ, Dept Mech Engn, Beijing, Peoples R China
4.Tsinghua Univ, State Key Lab Tribol, Beijing, Peoples R China
5.HIECISE Precis Equipment Co Ltd, Kunshan, Peoples R China
6.Southern Univ Sci & Technol, Mech & Energy Engn, Shenzhen, Peoples R China

Shi, Depeng,Zhou, Jiehan,Xu, Jirui,et al. Machine Vision-based Segmentation and Classification Method for Intelligent Roller Surface Monitoring[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2018:1811-1817.