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Computer Vision-Based Bridge Displacement Measurements Using Rotation-Invariant Image Processing Technique

Author

Listed:
  • Byung-Wan Jo

    (Department of Civil and Environmental Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Korea)

  • Yun-Sung Lee

    (Department of Civil and Environmental Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Korea)

  • Jun Ho Jo

    (Department of Civil and Environmental Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Korea)

  • Rana Muhammad Asad Khan

    (Department of Civil and Environmental Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Korea)

Abstract

Bridges are exposed to various kinds of external loads, including vehicle and hurricanes, during their life cycle. These loads cause structural damage, which may lead to bridge collapse. To ensure bridge safety, it is essential to periodically inspect the physical and functional conditions of bridges. The displacement responses of a bridge have significance in determining the structural behaviors and assessing their safety. In recent years, many researchers have been studying bridge displacement measurements using image processing technologies. Image-processing-based displacement measurements using a computer analysis system can quickly assess bridge conditions and, thus, can be used to enhance the reliability of bridges with high accuracy. This paper presents a method based on multiple-image processing bridge displacement measurements that includes enhanced robustness to image rotation. This study applies template matching and a homography matrix to measure the displacement that works well regardless of the angle between the smartphone camera and the target.

Suggested Citation

  • Byung-Wan Jo & Yun-Sung Lee & Jun Ho Jo & Rana Muhammad Asad Khan, 2018. "Computer Vision-Based Bridge Displacement Measurements Using Rotation-Invariant Image Processing Technique," Sustainability, MDPI, vol. 10(6), pages 1-16, May.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:6:p:1785-:d:149588
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    Citations

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    Cited by:

    1. Sang-Ho Cho & Kyung-Tae Lee & Se-Heon Kim & Ju-Hyung Kim, 2019. "Image Processing for Sustainable Remodeling: Introduction to Real-time Quality Inspection System of External Wall Insulation Works," Sustainability, MDPI, vol. 11(4), pages 1-17, February.
    2. Edmundas Kazimieras Zavadskas & Jonas Šaparauskas & Jurgita Antucheviciene, 2018. "Sustainability in Construction Engineering," Sustainability, MDPI, vol. 10(7), pages 1-7, June.

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