
Proceedings Paper
A novel background subtraction technique based on grayscale morphology for weld defect detectionFormat | Member Price | Non-Member Price |
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Paper Abstract
Optical inspection is a non-destructive quality monitoring technique to detect defects in manufactured parts. Automating the defect detection, by application of image processing, prevents the presence of human operators making the inspection more reliable, reproducible and faster. In this paper, a background subtraction technique, based on morphological operations, is proposed. The low-computational load associated with the used morphological operations makes this technique more computationally effective than background subtraction techniques such as spline approximation and surface-fitting. The performance of the technique is tested by applying to detect defects in a weld seam with non-uniform intensity distribution where the defects are precisely segmented. The proposed background subtraction technique is generalizable to sheet, surface, or part defect detection in various applications of manufacturing.
Paper Details
Date Published: 8 April 2016
PDF: 5 pages
Proc. SPIE 9804, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, and Civil Infrastructure 2016, 98041E (8 April 2016); doi: 10.1117/12.2223262
Published in SPIE Proceedings Vol. 9804:
Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, and Civil Infrastructure 2016
Tzuyang Yu; Andrew L. Gyekenyesi; Peter J. Shull; H. Felix Wu, Editor(s)
PDF: 5 pages
Proc. SPIE 9804, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, and Civil Infrastructure 2016, 98041E (8 April 2016); doi: 10.1117/12.2223262
Show Author Affiliations
Masoumeh Aminzadeh, Georgia Institute of Technology (United States)
Thomas Kurfess, Georgia Institute of Technology (United States)
Published in SPIE Proceedings Vol. 9804:
Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, and Civil Infrastructure 2016
Tzuyang Yu; Andrew L. Gyekenyesi; Peter J. Shull; H. Felix Wu, Editor(s)
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