MATEC Web of Conferences (Jan 2021)

A Review of Computer Vision Techniques in the Detection of Metal Failures

  • Fitzgerald Deborah,
  • Fragoudakis Roselita

DOI
https://doi.org/10.1051/matecconf/202134902021
Journal volume & issue
Vol. 349
p. 02021

Abstract

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This paper considers and contrasts several computer vision techniques used to detect defects in metallic components during manufacturing or in service. Methodologies include statistical analysis, weighted entropy modification, Fourier transformations, neural networks, and deep learning. Such systems are used by manufacturers to perform non-destructive testing and inspection of components at high speeds [1]; providing better error detection than traditional human visual inspection, and lower costs [2]. This is a review of the computer vision system comparing different mathematical analysis in order to illustrate the strengths and weaknesses relative to the nature of the defect. It includes exemplar that histograms and statistical analysis operate best with significant contrast between the defect and background, that co-occurrence matrix and Gabor filtering are computationally expensive, that structural analysis is useful when there are repeated patterns, that Fourier transforms, applied to spatial data, need windowing to capture localized issues, and that neural networks can be utilized after training.