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 paper 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, 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 literatures 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.
Luo Qiwu, Fang Xiaoxin, Liu Li, Yang Chunhua, Sun Yichuang
A1 Journal article – refereed
Place of publication:
Q. Luo, X. Fang, L. Liu, C. Yang and Y. Sun, “Automated Visual Defect Detection for Flat Steel Surface: A Survey,” in IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 3, pp. 626-644, March 2020, https://doi.org/10.1109/TIM.2019.2963555
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