A Survey of Vision-Based Methods for Surface Defects’ Detection and Classification in Steel Products

In the competitive landscape of steel-strip production, ensuring the high quality of steel surfaces is paramount. Traditionally, human visual inspection has been the primary method for detecting defects, but it suffers from limitations such as reliability, cost, processing time, and accuracy. Visual...

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Bibliographic Details
Published in:Informatics (Basel) 2024-06, Vol.11 (2), p.25
Main Authors: Ibrahim, Alaa Aldein M. S., Tapamo, Jules-Raymond
Format: Article
Language:English
Subjects:
Algorithms
Automation
Classification
Deep learning
defect classification
defect detection
Defects
image acquisition
Image processing
Inspection
Machine learning
Machine vision
Manufacturing
Metal strips
Product quality
Productivity
Quality control
Quality management
Statistical analysis
Statistical methods
Steel products
Strip steel
Surface defects
Vision
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Summary:In the competitive landscape of steel-strip production, ensuring the high quality of steel surfaces is paramount. Traditionally, human visual inspection has been the primary method for detecting defects, but it suffers from limitations such as reliability, cost, processing time, and accuracy. Visual inspection technologies, particularly automation techniques, have been introduced to address these shortcomings. This paper conducts a thorough survey examining vision-based methodologies related to detecting and classifying surface defects on steel products. These methodologies encompass statistical, spectral, texture segmentation based methods, and machine learning-driven approaches. Furthermore, various classification algorithms, categorized into supervised, semi-supervised, and unsupervised techniques, are discussed. Additionally, the paper outlines the future direction of research focus.
ISSN:2227-9709
2227-9709
DOI:10.3390/informatics11020025