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Automated Visual Defect Detection for Flat Steel Surface: A Survey

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Automated Visual Defect Detection for Flat Steel Surface: A Survey. / Luo, Qiwu ; Fang, Xiaoxin; Liu, Li; Yang, Chunhua; Sun, Yichuang.

In: IEEE Transactions on Instrumentation and Measurement, Vol. 69, No. 3, 8948233, 01.01.2020, p. 626-644.

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Luo, Qiwu ; Fang, Xiaoxin ; Liu, Li ; Yang, Chunhua ; Sun, Yichuang. / Automated Visual Defect Detection for Flat Steel Surface: A Survey. In: IEEE Transactions on Instrumentation and Measurement. 2020 ; Vol. 69, No. 3. pp. 626-644.

Bibtex

@article{6b54b77ed1be46dca5dd532888d32d76,
title = "Automated Visual Defect Detection for Flat Steel Surface: A Survey",
abstract = "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 article 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 and 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 works 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.",
keywords = "Automated optical inspection (AOI), automated visual inspection (AVI), flat steel, surface defect detection, survey",
author = "Qiwu Luo and Xiaoxin Fang and Li Liu and Chunhua Yang and Yichuang Sun",
note = "{\textcopyright} 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. ",
year = "2020",
month = jan,
day = "1",
doi = "10.1109/TIM.2019.2963555",
language = "English",
volume = "69",
pages = "626--644",
journal = "IEEE Transactions on Instrumentation and Measurement",
issn = "0018-9456",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "3",

}

RIS

TY - JOUR

T1 - Automated Visual Defect Detection for Flat Steel Surface: A Survey

AU - Luo, Qiwu

AU - Fang, Xiaoxin

AU - Liu, Li

AU - Yang, Chunhua

AU - Sun, Yichuang

N1 - © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2020/1/1

Y1 - 2020/1/1

N2 - 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 article 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 and 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 works 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.

AB - 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 article 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 and 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 works 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.

KW - Automated optical inspection (AOI)

KW - automated visual inspection (AVI)

KW - flat steel

KW - surface defect detection

KW - survey

UR - http://www.scopus.com/inward/record.url?scp=85080951489&partnerID=8YFLogxK

U2 - 10.1109/TIM.2019.2963555

DO - 10.1109/TIM.2019.2963555

M3 - Article

VL - 69

SP - 626

EP - 644

JO - IEEE Transactions on Instrumentation and Measurement

JF - IEEE Transactions on Instrumentation and Measurement

SN - 0018-9456

IS - 3

M1 - 8948233

ER -