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A Vision-Based Sensor for Noncontact Structural Displacement Measurement.

Feng D, Feng MQ, Ozer E, Fukuda Y - Sensors (Basel) (2015)

Bottom Line: An advanced template matching algorithm, referred to as the upsampled cross correlation, is adopted and further developed into a software package for real-time displacement extraction from video images.By simply adjusting the upsampling factor, better subpixel resolution can be easily achieved to improve the measurement accuracy.Then field tests are carried out on a railway bridge and a pedestrian bridge, through which the accuracy of the vision sensor in both time and frequency domains is further confirmed in realistic field environments.

View Article: PubMed Central - PubMed

Affiliation: Department of Civil Engineering and Engineering Mechanics, Columbia University, New York, NY 10027, USA. df2465@columbia.edu.

ABSTRACT
Conventional displacement sensors have limitations in practical applications. This paper develops a vision sensor system for remote measurement of structural displacements. An advanced template matching algorithm, referred to as the upsampled cross correlation, is adopted and further developed into a software package for real-time displacement extraction from video images. By simply adjusting the upsampling factor, better subpixel resolution can be easily achieved to improve the measurement accuracy. The performance of the vision sensor is first evaluated through a laboratory shaking table test of a frame structure, in which the displacements at all the floors are measured by using one camera to track either high-contrast artificial targets or low-contrast natural targets on the structural surface such as bolts and nuts. Satisfactory agreements are observed between the displacements measured by the single camera and those measured by high-performance laser displacement sensors. Then field tests are carried out on a railway bridge and a pedestrian bridge, through which the accuracy of the vision sensor in both time and frequency domains is further confirmed in realistic field environments. Significant advantages of the noncontact vision sensor include its low cost, ease of operation, and flexibility to extract structural displacement at any point from a single measurement.

No MeSH data available.


Error resulting from camera non-perpendicularity: (a) Effect of optical axis tilt angle (f = 50 mm); (b) Effect of focal length of lens (θ = 3°).
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sensors-15-16557-f003: Error resulting from camera non-perpendicularity: (a) Effect of optical axis tilt angle (f = 50 mm); (b) Effect of focal length of lens (θ = 3°).

Mentions: From the scaling factors in Equation (2) and in Equation (4), the “measured displacement” can be estimated by or . In order to quantify the error resulting from camera non-perpendicularity, numerical studies are conducted. The measurement errors from the two scaling factors can be defined as: . The adopted parameters are: camera with 640 × 512 pixel resolution, , and , D = 10 m. Point C has a 1 pixel translation in the image plane from to . The effects of the optical axis tilt angle and lens focal length are investigated by considering a variable range and the results are shown in Figure 3. It can be seen that the error increases as the tilt angle increases and the error is inversely related to the focal length. In sum, it could be concluded that in most practical applications the measurement errors from small optical tilt angles are acceptable. Although this study is based on the 1D (x axis) in-plane translation, the conclusions can be equally extended to the 2D in-plane translation.


A Vision-Based Sensor for Noncontact Structural Displacement Measurement.

Feng D, Feng MQ, Ozer E, Fukuda Y - Sensors (Basel) (2015)

Error resulting from camera non-perpendicularity: (a) Effect of optical axis tilt angle (f = 50 mm); (b) Effect of focal length of lens (θ = 3°).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541893&req=5

sensors-15-16557-f003: Error resulting from camera non-perpendicularity: (a) Effect of optical axis tilt angle (f = 50 mm); (b) Effect of focal length of lens (θ = 3°).
Mentions: From the scaling factors in Equation (2) and in Equation (4), the “measured displacement” can be estimated by or . In order to quantify the error resulting from camera non-perpendicularity, numerical studies are conducted. The measurement errors from the two scaling factors can be defined as: . The adopted parameters are: camera with 640 × 512 pixel resolution, , and , D = 10 m. Point C has a 1 pixel translation in the image plane from to . The effects of the optical axis tilt angle and lens focal length are investigated by considering a variable range and the results are shown in Figure 3. It can be seen that the error increases as the tilt angle increases and the error is inversely related to the focal length. In sum, it could be concluded that in most practical applications the measurement errors from small optical tilt angles are acceptable. Although this study is based on the 1D (x axis) in-plane translation, the conclusions can be equally extended to the 2D in-plane translation.

Bottom Line: An advanced template matching algorithm, referred to as the upsampled cross correlation, is adopted and further developed into a software package for real-time displacement extraction from video images.By simply adjusting the upsampling factor, better subpixel resolution can be easily achieved to improve the measurement accuracy.Then field tests are carried out on a railway bridge and a pedestrian bridge, through which the accuracy of the vision sensor in both time and frequency domains is further confirmed in realistic field environments.

View Article: PubMed Central - PubMed

Affiliation: Department of Civil Engineering and Engineering Mechanics, Columbia University, New York, NY 10027, USA. df2465@columbia.edu.

ABSTRACT
Conventional displacement sensors have limitations in practical applications. This paper develops a vision sensor system for remote measurement of structural displacements. An advanced template matching algorithm, referred to as the upsampled cross correlation, is adopted and further developed into a software package for real-time displacement extraction from video images. By simply adjusting the upsampling factor, better subpixel resolution can be easily achieved to improve the measurement accuracy. The performance of the vision sensor is first evaluated through a laboratory shaking table test of a frame structure, in which the displacements at all the floors are measured by using one camera to track either high-contrast artificial targets or low-contrast natural targets on the structural surface such as bolts and nuts. Satisfactory agreements are observed between the displacements measured by the single camera and those measured by high-performance laser displacement sensors. Then field tests are carried out on a railway bridge and a pedestrian bridge, through which the accuracy of the vision sensor in both time and frequency domains is further confirmed in realistic field environments. Significant advantages of the noncontact vision sensor include its low cost, ease of operation, and flexibility to extract structural displacement at any point from a single measurement.

No MeSH data available.