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        Automated Visual Defect Detection for Flat Steel Surface: A Survey

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        08948233.pdf (PDF, 1Mb)
        Author
        Luo, Qiwu
        Fang, Xiaoxin
        Liu, Li
        Yang, Chunhua
        Sun, Yichuang
        Attention
        2299/22040
        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.
        Publication date
        2020-01-01
        Published in
        IEEE Transactions on Instrumentation and Measurement
        Published version
        https://doi.org/10.1109/TIM.2019.2963555
        Other links
        http://hdl.handle.net/2299/22040
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