University of Oulu

Q. Luo et al., "Automated Visual Defect Classification for Flat Steel Surface: A Survey," in IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 12, pp. 9329-9349, Dec. 2020, doi: 10.1109/TIM.2020.3030167

Automated visual defect classification for flat steel surface : a survey

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Author: Luo, Qiwu1; Fang, Xiaoxin2; Su, Jiaojiao1;
Organizations: 1School of Automation, Central South University, Changsha 410083, China
2School of Electrical and Automation Engineering, Hefei University of Technology, Hefei 230009, China
3College of System Engineering, National University of Defense Technology, Changsha 410073, China
4Center for Machine Vision and Signal analysis, University of Oulu 90014, Finland
5Powder Metallurgy Research Institute, Central South University, Changsha 410083, China
6RAMON Beijing Research Institute, RAMON Science and Technology Company Ltd., Beijing 100190, China
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.5 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe202103046496
Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2021-03-04
Description:

Abstract

For a typical surface automated visual inspection (AVI) instrument of planar materials, defect classification is an indispensable part after defect detection, which acts as a crucial precondition for achieving the online quality inspection of end products. In the industrial environment of manufacturing flat steels, this task is awfully difficult due to diverse defect appearances, ambiguous intraclass, and interclass distances. This article attempts to present a focused but systematic review of the traditional and emerging automated computer-vision-based defect classification methods by investigating approximately 140 studies on three specific flat steel products of con-casting slabs, hot-rolled steel strips, and cold-rolled steel strips. According to the natural image processing procedure of defect recognition, the diverse approaches are grouped into five successive parts: image acquisition, image preprocessing, feature extraction, feature selection, and defect classifier. Recent literature has been reviewed from an industrial goal-oriented perspective to provide some guidelines for future studies and recommend suitable methods for boosting the surface quality inspection level of AVI instruments.

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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: 12
Pages: 9329 - 9349
DOI: 10.1109/TIM.2020.3030167
OADOI: https://oadoi.org/10.1109/TIM.2020.3030167
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
Subjects:
Funding: This work was supported in part by the National Natural Science Foundation of China under Grant 61988101, Grant 61973323, and Grant 51704089, in part by the Hunan Provincial National Science Foundation of China under Grant 2020JJ4747, and in part by the Innovation and Development Project of Ministry of Industry and Information Technology of the People’s Republic of China under Grant TC19084DY
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