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Notice of retraction
Vol. 34, No. 8(3), S&M3042

Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
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Sensors and Materials, Volume 33, Number 3(2) (2021)
Copyright(C) MYU K.K.
pp. 995-1008
S&M2511 Research Paper
https://doi.org/10.18494/SAM.2021.3011
Published: March 12, 2021

Optical Crackmeter for Retaining Wall in a Landslide Area Using Computer Vision Technology [PDF]

Yu-Chin Chen, I-Hui Chen, Jun-Yang Chen, and Miau-Bin Su

(Received August 4, 2020; Accepted January 25, 2021)

Keywords: computer vision, crackmeter, landslide monitoring, IoT instrument

An innovative 3D optical crackmeter employing computer vision technology is used for displacement monitoring in a crack of a retaining wall automatically and remotely. The 3D optical crackmeter is composed of a Raspberry Pi device and a digital camera in a box, and a fixed chessboard on the two sides of a crack. A network with LoRa wireless communication can be connected as an IoT system to provide automatic remote functions. The OpenCV library is employed to analyze changes in chessboard imaging so that relative displacements of the crack in the retaining wall can be measured in a landslide area. Through laboratory and field testing, the resolution and accuracy of the 3D optical crackmeter were determined as 0.04 and 0.1 mm, respectively. Using the crackmeter, we observed significant displacements in the x- and z-directions of the crack in a retaining wall of 0.067 and 0.060 cm, respectively, in the Jhongsinlun landslide area of Taiwan over three months. Overall, the 3D optical crackmeter with computer vision technology can accurately measure the 3D displacement of cracks in a retaining wall. Moreover, the IoT-based 3D optical crackmeter is more cost-effective than traditional crackmeters used in landslide areas.

Corresponding author: I-Hui Chen


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This work is licensed under a Creative Commons Attribution 4.0 International License.

Cite this article
Yu-Chin Chen, I-Hui Chen, Jun-Yang Chen, and Miau-Bin Su, Optical Crackmeter for Retaining Wall in a Landslide Area Using Computer Vision Technology, Sens. Mater., Vol. 33, No. 3, 2021, p. 995-1008.



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