Automated Visual Defect Detection for Flat Steel Surface: A Survey

Qiwu  Luo; Xiaoxin Fang; Li Liu; Chunhua Yang; Yichuang Sun

doi:10.1109/TIM.2019.2963555

Automated Visual Defect Detection for Flat Steel Surface: A Survey

Qiwu Luo, Xiaoxin Fang, Li Liu, Chunhua Yang, Yichuang Sun

Research output: Contribution to journal › Article › peer-review

39 Citations (Scopus)

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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.

Original language	English
Article number	8948233
Pages (from-to)	626-644
Number of pages	19
Journal	IEEE Transactions on Instrumentation and Measurement
Volume	69
Issue number	3
Early online date	1 Jan 2020
DOIs	https://doi.org/10.1109/TIM.2019.2963555
Publication status	E-pub ahead of print - 1 Jan 2020

Keywords

Automated optical inspection (AOI)
automated visual inspection (AVI)
flat steel
surface defect detection
survey

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10.1109/TIM.2019.2963555Licence: Other

08948233
© 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.
Accepted author manuscript, 1.45 MBLicence: Other

Cite this

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Automated Visual Defect Detection for Flat Steel Surface: A Survey

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Keywords

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