Automated Visual Defect Detection for Flat Steel Surface: A Survey

By Qiwu Luo, Xiaoxin Fang, Li Liu, Chunhua Yang, Yichuang Sun. IEEE Transactions on Instrumentation and Measurement, 2020

437 citations recorded by the source corpus.

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.

Subfields: Civil and Structural Engineering, Computational Mechanics, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/f5b9b024b3e3d7207225365bd52a4a0666e0f9a6

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In Computational Mechanics

In Industrial and Manufacturing Engineering

Citations in the corpus

Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY (2020, methodology): “Xie et al. [2] categorized the techniques for extracting texture features into four categories with references and their comparison (based on the work of Luo et al. [3]) to distinct papers in this ﬁeld (see Tables 1 and 2), and then Ngan et al. [4] in 2011 made a new extended review.”