人工智能
计算机视觉
曲面(拓扑)
机器视觉
计算机科学
模式识别(心理学)
数学
几何学
作者
Alaa Aldein M. S. Ibrahim,Jules‐Raymond Tapamo
标识
DOI:10.3390/informatics11020025
摘要
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.
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