Nontarget Vision Sensor for Remote Measurement of Bridge Dynamic Response

Feng, Maria Q.; Fukuda, Yoshihiro; Feng, Dongming; Mizuta, Masato

doi:10.1061/(asce)be.1943-5592.0000747

Cited by 138 publications

(104 citation statements)

References 19 publications

Supporting

Mentioning

102

Contrasting

Unclassified

Order By: Relevance

“…The authors’ team previously developed the orientation code matching (OCM) technique, which uses gradient information of images to achieve robust displacement measurement against lighting changes in tracking artificial target (Fukuda et al., ). Upon validating its robustness in template matching against illumination change and occlusion, the OCM was further applied to measurements of bridge displacements in the field (Feng et al., ; Feng and Feng, ). However, orientation codes are quantified from gradient orientations, which may be invariant to different gradient magnitudes.…”

Section: Introductionmentioning

confidence: 99%

Edge‐Enhanced Matching for Gradient‐Based Computer Vision Displacement Measurement

Luo

Feng

2018

Computer aided Civil Eng

Self Cite

View full text Add to dashboard Cite

Computer vision‐based displacement measurement for structural monitoring has grown popular. However, tracking natural low‐contrast targets in low‐illumination conditions is inevitable for vision sensors in the field measurement, which poses challenges for intensity‐based vision‐sensing techniques. A new edge‐enhanced‐matching (EEM) technique improved from the previous orientation‐code‐matching (OCM) technique is proposed to enable robust tracking of low‐contrast features. Besides extracting gradient orientations from images as OCM, the proposed EEM technique also utilizes gradient magnitudes to identify and enhance subtle edge features to form EEM images. A ranked‐segmentation filtering technique is also developed to post‐process EEM images to make it easier to identify edge features. The robustness and accuracy of EEM in tracking low‐contrast features are validated in comparison with OCM in the field tests conducted on a railroad bridge and the long‐span Manhattan Bridge. Frequency domain analyses are also performed to further validate the displacement accuracy.

show abstract

Section: Introductionmentioning

confidence: 99%

Edge‐Enhanced Matching for Gradient‐Based Computer Vision Displacement Measurement

Luo

Feng

2018

Computer aided Civil Eng

Self Cite

View full text Add to dashboard Cite

show abstract

“…LVDT [10,55,103], laser sensors [55] and potentiometers [44] for short-span bridge and GPS [13,30,49] for long-span bridges. The displacement data could be interpreted for system identification [8,11,12,49,54,55,63,75], evaluation of load carrying capacity [53], model calibration [18] and estimation of vehicle weights [20].…”

Section: Application Examplesmentioning

confidence: 99%

“…The displacement data could be interpreted for system identification [8,11,12,49,54,55,63,75], evaluation of load carrying capacity [53], model calibration [18] and estimation of vehicle weights [20]. Artificial targets are commonly used in existing applications to assist camera calibration, whereas recent investigations [51,63,75,103,104] overcome the dependence on artificial targets and realise completely non-contact sensing based on a simplified projection transformation, i.e. scale factor.…”

Section: Application Examplesmentioning

confidence: 99%

Review of machine-vision based methodologies for displacement measurement in civil structures

Brownjohn

2017

J Civil Struct Health Monit

254

125

View full text Add to dashboard Cite

Vision-based systems are promising tools for displacement measurement in civil structures, possessing advantages over traditional displacement sensors in instrumentation cost, installation efforts and measurement capacity in terms of frequency range and spatial resolution. Approximately one hundred papers to date have appeared on this subject, investigating topics like system development and improvement, the viability on field applications and the potential for structural condition assessment. The main contribution of this paper is to present a literature review of vision-based displacement measurement, from the perspectives of methodologies and applications. Video-processing procedures in this paper are summarised as a three-component framework: camera calibration, target tracking and structural displacement calculation. Methods for each component are presented in principle, with discussions about the relative advantages and limitations. Applications in the two most active fields, bridge deformation and cable vibration measurement, are examined followed by a summary of field challenges observed in monitoring tests. Important gaps requiring further investigation are presented, e.g. robust tracking methods, non-contact sensing and measurement accuracy evaluation in field conditions.

show abstract

“…In other words, if the marker were to be removed, it should be possible to calculate the scaling factor that converts the coordinates of the feature point without markers and a method of extracting the feature point without markers should be available. To solve the problem in which the existing VDS is accompanied by a marker, several works were conducted on extracting the displacement data [39,40,41] from some natural markers such as a rivet or a bolt hole of the structure in the image as feature points, and the dynamic characteristics of the structure were extracted precisely using the displacement data obtained from the image without artificial markers. In these works, a technique that was an extension of digital image correlation (DIC) was introduced to identify the feature points without artificial markers, and the scaling factor was calculated based on the focal length of the camera as well as the distance and angle between the camera and structure.…”

Section: Introductionmentioning

confidence: 99%

A Target-Less Vision-Based Displacement Sensor Based on Image Convex Hull Optimization for Measuring the Dynamic Response of Building Structures

Choi

Kim

2016

Sensors

View full text Add to dashboard Cite

Existing vision-based displacement sensors (VDSs) extract displacement data through changes in the movement of a target that is identified within the image using natural or artificial structure markers. A target-less vision-based displacement sensor (hereafter called “TVDS”) is proposed. It can extract displacement data without targets, which then serve as feature points in the image of the structure. The TVDS can extract and track the feature points without the target in the image through image convex hull optimization, which is done to adjust the threshold values and to optimize them so that they can have the same convex hull in every image frame and so that the center of the convex hull is the feature point. In addition, the pixel coordinates of the feature point can be converted to physical coordinates through a scaling factor map calculated based on the distance, angle, and focal length between the camera and target. The accuracy of the proposed scaling factor map was verified through an experiment in which the diameter of a circular marker was estimated. A white-noise excitation test was conducted, and the reliability of the displacement data obtained from the TVDS was analyzed by comparing the displacement data of the structure measured with a laser displacement sensor (LDS). The dynamic characteristics of the structure, such as the mode shape and natural frequency, were extracted using the obtained displacement data, and were compared with the numerical analysis results. TVDS yielded highly reliable displacement data and highly accurate dynamic characteristics, such as the natural frequency and mode shape of the structure. As the proposed TVDS can easily extract the displacement data even without artificial or natural markers, it has the advantage of extracting displacement data from any portion of the structure in the image.

show abstract

Nontarget Vision Sensor for Remote Measurement of Bridge Dynamic Response

Cited by 138 publications

References 19 publications

Edge‐Enhanced Matching for Gradient‐Based Computer Vision Displacement Measurement

Edge‐Enhanced Matching for Gradient‐Based Computer Vision Displacement Measurement

Review of machine-vision based methodologies for displacement measurement in civil structures

A Target-Less Vision-Based Displacement Sensor Based on Image Convex Hull Optimization for Measuring the Dynamic Response of Building Structures

Contact Info

Product

Resources

About