Digital image correlation for surface deformation measurement: historical developments, recent advances and future goals

Evaluation of accuracy of as-built 3D modeling from photos taken by handheld digital cameras

In the past two decades, a significant number of innovative sensing and monitoring systems based on the machine vision-based technology have been exploited in the field of structural health monitoring (SHM). This technology has some inherent distinctive advantages such as noncontact, nondestructive, long distance, high precision, immunity to electromagnetic interference, and large-range and multiple-target monitoring. A lot of machine vision-based structural dynamic measurement and structural state inspection methods have been proposed. Real-world applications are also carried out to measure the structural physical parameters such as the displacement, strain/stress, rotation, vibration, crack, and spalling. The purpose of this review article is devoted to presenting a summary of the basic theories and practical applications of the machine vision-based technology employed in structural monitoring as well as its systematic error sources and integration with other modern sensing techniques.

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A Review of Machine Vision-Based Structural Health Monitoring: Methodologies and Applications

Photogrammetric methods for dense 3D surface reconstruction are increasingly available to both professional and amateur users who have requirements that span a wide variety of applications. One of the key concerns in choosing an appropriate method is to understand the achievable accuracy and how choices made within the workflow can alter that outcome. In this paper we consider accuracy in two components: the ability to generate a correctly scaled 3D model; and the ability to automatically deliver a high quality data set that provides good agreement to a reference surface. The determination of scale information is particularly important, since a network of images usually only provides angle measurements and thus leads to unscaled geometry. A solution is the introduction of known distances in object space, such as base lines between camera stations or distances between control points. In order to avoid using known object distances, the method presented in this paper exploits a calibrated stereo camera utilizing the calibrated base line information from the camera pair as an observational based geometric constraint. The method provides distance information throughout the object volume by orbiting the object. In order to test the performance of this approach, four topical surface matching methods have been investigated to determine their ability to produce accurate, dense point clouds. The methods include two versions of Semi-Global Matching as well as MicMac and Patch-based Multi-View Stereo (PMVS). These methods are implemented on a set of stereo images captured from four carefully selected objects by using (1) an off-the-shelf low cost 3D camera and (2) a pair of Nikon D700 DSLR cameras rigidly mounted in close proximity to each other. Inter-comparisons demonstrate the subtle differences between each of these permutations. The point clouds are also compared to a dataset obtained with a Nikon MMD laser scanner. Finally, the established process of achieving accurate point clouds from images and known object space distances are compared with the presented strategies. Results from the matching demonstrate that if a good imaging network is provided, using a stereo camera and bundle adjustment with geometric constraints can effectively resolve the scale. Among the strategies for dense 3D reconstruction, using the presented method for solving the scale problem and PMVS on the images captured with two DSLR cameras resulted in a dense point cloud as accurate as the Nikon laser scanner dataset.

A comparison of dense matching algorithms for scaled surface reconstruction using stereo camera rigs

Accurate measurement with photogrammetry at large sites

Large deformation measurement scheme for 3D digital image correlation method

A three-dimensional (3-D) digital image correlation system for deformation measurement in experimental mechanics has been developed. The key technologies applied in the system are discussed in detail, including stereo camera calibration, digital image correlation, 3-D reconstruction, and 3-D displacement/strain computation. A stereo camera self-calibration algorithm based on photogrammetry is proposed. In the algorithm, the interior and exterior orientation parameters of stereo cameras and the 3-D coordinates of calibration target points are estimated together, using the bundle adjustment technique, so the 3-D coordinates of calibration target points are not needed in advance to get a reliable camera calibration result. An efficient image correlation scheme with high precision is developed using the iterative least-squares nonlinear optimization algorithm, and a method based on a seed point is proposed to provide a reliable initial value for the nonlinear optimization. After the 3-D coordinates of the object points are calculated using the triangulation method, the 3-D displacement/strain field could then be obtained from them. After calibration, the system accuracy for static profile, displacement, and strain measurement is evaluated through a series of experiments. The experiment results confirm that the proposed system is accurate and reliable for deformation measurement in experimental mechanics.

Zhenzhong Xiao

Papers

Large deformation measurement scheme for 3D digital image correlation method

Three-dimensional digital image correlation system for deformation measurement in experimental mechanics

A cross-target-based accurate calibration method of binocular stereo systems with large-scale field-of-view

An accurate stereo vision system using cross-shaped target self-calibration method based on photogrammetry

A four-camera videogrammetric system for 3-D motion measurement of deformable object