University of Hertfordshire

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Automated Visual Defect Detection for Flat Steel Surface: A Survey

Research output: Contribution to journalArticlepeer-review


  • 08948233

    Accepted author manuscript, 1.45 MB, PDF document

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Original languageEnglish
Article number8948233
Pages (from-to)626-644
Number of pages19
JournalIEEE Transactions on Instrumentation and Measurement
Early online date1 Jan 2020
Publication statusE-pub ahead of print - 1 Jan 2020


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.


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