Review of vision-based steel surface inspection systems

By Nirbhar Neogi, Dusmanta Kumar Mohanta, Pranab Kumar Dutta. EURASIP Journal on Image and Video Processing, 2014

307 citations recorded by the source corpus.

Steel is the material of choice for a large number and very diverse industrial applications. Surface qualities along with other properties are the most important quality parameters, particularly for flat-rolled steel products. Traditional manual surface inspection procedures are awfully inadequate to ensure guaranteed quality-free surface. To ensure stringent requirements of customers, automated vision-based steel surface inspection techniques have been found to be very effective and popular during the last two decades. Considering its importance, this paper attempts to make the first formal review of state-of-art of vision-based defect detection and classification of steel surfaces as they are produced from steel mills. It is observed that majority of research work has been undertaken for cold steel strip surfaces which is most sensitive to customers' requirements. Work on surface defect detection of hot strips and bars/rods has also shown signs of increase during the last 10 years. The review covers overall aspects of automatic steel surface defect detection and classification systems using vision-based techniques. Attentions have also been drawn to reported success rates along with issues related to real-time operational aspects.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/b20fcdf082da73ffc3a5ceaff617810e1922d06d

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In Computational Mechanics

In Computer Vision and Pattern Recognition

In Industrial and Manufacturing Engineering

Citations in the corpus

NB-CNN: Deep Learning-Based Crack Detection Using Convolutional Neural Network and Naïve Bayes Data Fusion (2018, methodology): “Recent vision-based steel surface inspection systems have been reviewed in [3] including crack detection algorithms.”
Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection (2016, methodology): “[2], existing methods roughly be divided into four main categories: statistical, structu filter based, and model based.”
Automated Visual Defect Detection for Flat Steel Surface: A Survey (2020, background): “Notably, a brief but rare AVI review covering defect detection and classification techniques for steel products was reported [9], where nearly all types of steel products (slab, billet, plate, hot strip, cold strip, rod/bar) are involved at an overview level.”