Computer vision–based sensors for the tilt monitoring of an underground structure in a landslide area

This study explores an innovative instrument to monitor potential tilting for an underground structure in a landslide area using computer vision technology. The instrument was composited of the Raspberry Pi, digital cameras, chessboards, and OpenCV programming. Image recognition technology was emplo...

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Bibliographic Details
Published in:Landslides 2020-04, Vol.17 (4), p.1009-1017
Main Authors: Chen, I-Hui, Lin, Yu-Shu, Su, Miau-Bin
Format: Article
Language:English
Subjects:
Accuracy
Agriculture
Attitude (inclination)
Cameras
Civil Engineering
Computer vision
Digital cameras
Digital imaging
Drainage
Earth and Environmental Science
Earth Sciences
Fiber optics
Field study
Geography
Internet of Things
Laboratories
Laboratory tests
Landslides
Landslides & mudslides
Libraries
Microelectromechanical systems
Monitoring
Monitoring instruments
Natural Hazards
Object recognition
Sensors
Strain gauges
Technical Note
Technology
Testing
Tiltmeters
Tunnel construction
Tunnels
Underground structures
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Summary:This study explores an innovative instrument to monitor potential tilting for an underground structure in a landslide area using computer vision technology. The instrument was composited of the Raspberry Pi, digital cameras, chessboards, and OpenCV programming. Image recognition technology was employed for detection of an inclined angle relevant to pixel changes of the center point on a chessboard in a computer vision–based tiltmeter. For laboratory testing, the resolution and accuracy of the tiltmeter were 0.01 and 0.03°, respectively. For field testing, five computer vision tiltmeters were installed in a drainage tunnel that was constructed under the ground in 80-m depth within a landslide area to monitor potential tilting of the tunnel from January to October in 2018. Maximum change in inclined angles of the five computer vision tiltmeters was 0.04° in the period. Overall, the computer vision tiltmeter for the inclined monitoring of the drainage tunnel is more cost-effective, real-time, and IoT-based than traditional monitoring devices in the landslide area.
ISSN:1612-510X
1612-5118
DOI:10.1007/s10346-019-01329-x