University of Oulu

Q. Luo, X. Fang, L. Liu, C. Yang and Y. Sun, "Automated Visual Defect Detection for Flat Steel Surface: A Survey," in IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 3, pp. 626-644, March 2020,

Automated visual defect detection for flat steel surface : a survey

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Author: Luo, Qiwu1; Fang, Xiaoxin2; Liu, Li3,4;
Organizations: 1chool of Automation, Central South University, Changsha 410083, China
2School of Electrical and Automation Engineering, Hefei University of Technology, Hefei 230009, China
3Center for Machine Vision and Signal Analysis, University of Oulu, Oulu 90014, Finland
4College of System Engineering, National University of Defense Technology, Changsha 410073, China
5School of Automation, Central South University, Changsha 410083, China
6School of Engineering and Computer Science, University of Hertfordshire, Hatfield ALl0 9AB, U.K.
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.3 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2020-01-30


Automated computer-vision-based defect detection has received much attention with the increasing surface quality assurance demands for the industrial manufacturing of flat steels. This paper attempts to present a comprehensive survey on surface defect detection technologies by reviewing about 120 publications over the last two decades for three typical flat steel products of con-casting slabs, hot- and cold-rolled steel strips. According to the nature of algorithms as well as image features, the existing methodologies are categorized into four groups: Statistical, spectral, model-based and machine learning. These literatures are summarized in this review to enable easy referral to suitable methods for diverse application scenarios in steel mills. Realization recommendations and future research trends are also addressed at an abstract level.

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Series: IEEE transactions on instrumentation and measurement
ISSN: 0018-9456
ISSN-E: 1557-9662
ISSN-L: 0018-9456
Volume: 69
Issue: 3
Pages: 626 - 644
DOI: 10.1109/TIM.2019.2963555
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Funding: This work was supported in part by the National Natural Science Foundation of China under Grant 51704089 and Grant 61973323, in part by the Anhui Provincial Natural Science Foundation of China under Grant 1808085QF190.
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