High-precision dimensional measurement of a curtain wall cross-section using image super-resolution

High-precision dimensional measurement of a curtain wall cross-section using image super-resolution

Jun Su Park, Hyo Seon Park

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Abstract. Dimensional quality is critical to the successful installation of curtain walls, and the required dimensional tolerances are typically less than a millimeter. However, high-precision dimensional measurement of a curtain wall cross-section is practically difficult and time-consuming because the cross-sectional shapes are various and complicated, and dimensional measurement is usually performed manually in the actual field. To improve these problems, various vision-based methos are being attempted, but there have been limitations in terms of precision due to row image resolution. Therefore, this study confirmed whether image super-resolution can contribute to overcoming these limitations. To this end, an experiment on a curtain wall profile cross-section was conducted, and super-resolution generative adversarial network (SRGAN) was applied as an image super-resolution method. As a result, it was confirmed that high-precision dimensional measurement is possible from an image with enhanced resolution using SRGAN.

Keywords
High-Precision Dimensional Measurement, Curtain Wall, Super-Resolution

Published online 3/30/2023, 5 pages
Copyright © 2023 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: Jun Su Park, Hyo Seon Park, High-precision dimensional measurement of a curtain wall cross-section using image super-resolution, Materials Research Proceedings, Vol. 27, pp 223-227, 2023

DOI: https://doi.org/10.21741/9781644902455-28

The article was published as article 28 of the book Structural Health Monitoring

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

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