On improving the accuracy of self-calibrated stereo digital image correlation system

Thiruselvam, N. Iniyan; Subramanian, Subramanian

doi:10.1088/1361-6501/abae3b

Cited by 7 publications

(5 citation statements)

References 41 publications

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“…For experimental comparison, we compute strains over the same displacement fields measured using the commercial software VIC-3D [32]; thus, facilitating direct comparison between VIC-3D [32] and the proposed method. As recommended in [21], VSG study 8 is performed to choose the best VSG 9 . The principal strains returned by the proposed method using the chosen VSG are compared to the strain gauge readings to validate the proposed method.…”

Section: Resultsmentioning

confidence: 99%

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Computation of strains from stereo digital image correlation using principal component analysis

Sharma¹,

Thiruselvam²,

Subramanian³

et al. 2021

Meas. Sci. Technol.

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Stereo digital image correlation (stereo DIC), a full-field deformation measurement technique, is widely used in the mechanics community due to its accuracy, versatility, and ability to measure 3D displacements over specimen surfaces. There are several strategies reported in the literature to compute strains from DIC displacements. Among them, the one based on principal component analysis (PCA) offers a way to effectively demarcate noise from the data by identifying the number of dominant singular vectors that are further differentiated by fitting an appropriate polynomial function. Though this approach has been used to differentiate 2D-DIC displacements, its implementation in the stereo-DIC workflow has not been reported yet. Moreover, its two important parameters, namely, the number of dominant singular vectors and the order of polynomial function, are still selected based on a heuristic where the user assesses the shape of the singular vectors and values to choose these parameters. In the present work, we address these gaps by presenting our novel PCA-based strain computation approach to stereo DIC, and automate the selection of parameters in our method using existing routines such as Stein's unbiased risk estimator (SURE) and Bayesian information criterion (BIC). We verify the accuracy of the proposed approach on synthetic and DIC Challenge datasets and validate it on data from two experiments: uniaxial tension test and hydraulic inflation test. We observe an excellent agreement between the computed strains and the ground truth in all the cases. The main benefits of the proposed method are two-fold: (a) it leads to convergence in virtual strain gauge study and (b) it is non-parametric because its key parameters are chosen adaptively based on the data at hand.

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Section: Resultsmentioning

confidence: 99%

“…method on synthetic and DIC challenge datasets and validate it on uniaxial tension and hydraulic inflation tests. The DIC parameters, namely, subset size, step size, and strain window size 4 are selected by conducting virtual strain gauge (VSG) study [21], as recommended in GPG [11].…”

Section: Introductionmentioning

confidence: 99%

Computation of strains from stereo digital image correlation using principal component analysis

Sharma¹,

Thiruselvam²,

Subramanian³

et al. 2021

Meas. Sci. Technol.

Self Cite

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“…Figure 3 shows a graphical representation of the planimetric and altimetric errors. The planimetric and altimetric accuracy is highly influenced by the camera parameters such as distance and focal plane [55]. Hence, the calibration of these parameters is crucial in UAV photogrammetry.…”

Section: Altimetric Errormentioning

confidence: 99%

Performance and precision analysis of 3D surface modeling through UAVs: validation and comparison of different photogrammetric data processing software

Mora-Félix,

Rangel-Peraza,

Monjardín-Armenta

et al. 2024

Phys. Scr.

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The planimetric and altimetric accuracies of the Digital Elevation Model (DEM) obtained from Unmanned Aerial Vehicle (UAV) depend on some photogrammetric parameters such as flight height, flight speed, and/or ground sample distance. In the last few years, the study of computer vision algorithms has increased focusing on their importance on the photogrammetric reconstruction process for land surface mapping. According to this, the main goal of this paper was to evaluate the performance of different photogrammetric processing software, such as Open Drone Map (ODM), Agisoft PhotoScan, and Pix4D, on DEM accuracy. For this purpose, a DJI Phantom 4 Pro drone was used for the acquisition of 600 images in a difficult topography area (630 ha) with 27 Ground Control Points (GCP) previously established. The photogrammetric products were generated, and a statistical analysis was carried out for the comparison of the DEMs. The results of the Root Mean Square Error (RMSE) show that the planimetric and altimetric accuracy of Agisoft PhotoScan (RMSExy = 0.514 m; RMSEz = 0.162 m) is greater than those obtained using Pix4D and ODM. Unsatisfactory results were obtained with ODM since deformations and high planimetric and altimetric errors were identified in the orthomosaic and DEM, which was related to inefficient key point detection and an incomplete mosaic construction process. Agisoft PhotoScan modeled the anthropogenic objects as part of the land surface, while Pix4d smoothed the terrain, eliminating these anthropogenic objects and not considering them as part of the ground surface.

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“…three-dimensional (3D) space. Existing calibration methods are broadly divided into target-based methods [3,4] and selfcalibration methods [5,6]. Target-based methods can achieve high-accuracy calibration with high-precision targets and are widely applied.…”

Section: Introductionmentioning

confidence: 99%

Calibration method for binocular vision system with large field of view based on small target image splicing

Li,

Xu,

Zhang

et al. 2024

Meas. Sci. Technol.

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In vision measurement, camera calibration has a significant impact on measurement precision. The classical target-based calibration methods require the target to occupy more than one-third of the field of view. A small-size target that does not meet the requirements results in poor calibration accuracy, while an appropriate large-size target is difficult to manufacture and inconvenient to operate. In view of the above problem, we propose a flexible and accurate calibration method based on small target image splicing to calibrate the binocular vision system with a large field of view. The spliced images and virtual large targets are constructed to extend the target size, providing better flexibility for calibration. Moreover, an optimization objective function integrating two constraints in the imaging plane and measurement space is presented to improve the calibration accuracy during the parameter optimization process. The simulation experiments and actual experiments are carried out to test the performance of the proposed method. The results demonstrate that the calibration accuracy of the proposed method using a small target is equivalent to that of Zhang’s method using a large target. Additionally, when using a same-size target, the parameter error of the proposed method is less than that of Zhang’s method, and the proposed method reduces the distance measurement error from 1.169mm to 0.208mm compared to Zhang's method.

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On improving the accuracy of self-calibrated stereo digital image correlation system

Cited by 7 publications

References 41 publications

Computation of strains from stereo digital image correlation using principal component analysis

Computation of strains from stereo digital image correlation using principal component analysis

Performance and precision analysis of 3D surface modeling through UAVs: validation and comparison of different photogrammetric data processing software

Calibration method for binocular vision system with large field of view based on small target image splicing

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