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International Journal of Sensors, Wireless Communications and Control

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ISSN (Print): 2210-3279
ISSN (Online): 2210-3287

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Research Article

Applying Computer Vision Technology to Triaxial Deformation Monitoring of Remedial Construction for a Landslide

Author(s): I-Hui Chen*, Shei-Chen Ho, Jun-Yang Chen, Yu-Shu Lin and Miau-Bin Su

Volume 9, Issue 4, 2019

Page: [488 - 496] Pages: 9

DOI: 10.2174/2210327909666190208153757

Price: $65

Abstract

Background & Objective: The paper explores a new instrument of computer vision to measure three-dimension deformation with an Internet of Things (IoT) system including Raspberry Pi, digital cameras and OpenCV programs in laboratory and field testing so as to monitor the potential deformation of a structure drainage well in a landslide.

Methods: A chessboard pattern is detected in the image by the camera so that pixels of chessboard cornors can be recognized by OpenCV programs. X-direction, Y-direction and Z-distance changes can be casulated by the similar triangles relationship of camera pixels. For laboratory testing, standard deviations of the measurement were approximately 0.01 cm.

Results: For field testing, the study installed four sets of Raspberry Pi in a drainage well within a landslide and employed OpenCV programs to interpret pixel changes of chessboards at four levels of the draiage well.

Conclusion: Overall, the instrument can be employed for triaxial deformation monitoring of the construction in the field effectively and automatically.

Keywords: Computer vision, ground deformation, image recognition, internet of things, landslide monitoring, raspberry pi.

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[1]
Malet JP, Maquaire O, Calais E. The use of global positioning system techniques for the continuous monitoring of landslides: Application to the super-sauze earthflow (Alpes-de-Haute-Provence, France). Geomorphology 2002; 43: 33-54.
[2]
Corsini A, Pasuto A, Soldati M, Zannoni A. Field monitoring of the Corvara landslide (Dolomites, Italy) and its relevance for hazard assessment. Geomorphology 2005; 66: 149-65.
[3]
Su MB, Chen IH, Liao CH. Using TDR cables and GPS for landslide monitoring in high mountain area. J Geotech Geoenviron Eng 2009; 135: 1113-21.
[4]
Brückl E, Brunner FK, Lang E, Mertl S, Müller M, Stary U. The Gradenbach observatory-monitoring deep-seated gravitational slope deformation by geodetic, hydrological, and seismological methods. Landslides 2013; 10: 815-29.
[5]
Miura S, Yamamoto T, Kuronuma I, Imai M. Vision metrology applied for configuration and displacement. Int J JCRM 2005; 1: 1-6.
[6]
Lee JJ, Shinozuka M. A vision-based system for remote sensing of bridge displacement. Ndt & E International 2006; 39: 425-31.
[7]
Khuc T, Catbas FN. Computer vision-based displacement and vibration monitoring without using physical target on structures. Struct Infrastruct Eng 2017; 13: 505-16.
[8]
Kaehler A, Bradskia G. Learning OpenCV: Computer vision with the OpenCV library. O'Reilly Media, Inc. 2008.
[9]
OpenCV API Reference. OpenCV Team; c2011-2014. Available from:https://docs.opencv.org/3.1.0/ [Updated 2014]
[10]
Open Source Computer Vision Library. GitHub Inc.; c2016. Available from https://github.com/opencv/opencv/tree/3.1.0 [Updated 2016].
[11]
Ahmmed P, Ahmed Z, Rafee MI, Awal MA, Choudhury SM. Self-localization of a mobile robot using monocular vision of a chessboard pattern. In 8th International Conference on Electrical and Computer Engineering. 2014: 753-6.
[12]
Zhang Z. A flexible new technique for camera calibration. IEEE Trans Pattern Anal Mach Intell 2000; 22: 1330-4.
[13]
Luhmann T, Robson S, Kyle S, Harley I. Close range photogrammetry. Wiley 2007.
[14]
Uchimura T, Towhata I, Wang L, et al. Precaution and early of surface failure of slopes using tilt sensors. Soil Found 2015; 55(5): 1086-99.
[15]
Uhlemann S, Smith A, Chambers J, et al. Assessment of ground-based monitoring techniques applied to landslide investigations. Geomorphology 2016; 253: 438-51.

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