Digital Image Correlation Algorithm Research Articles

Crack branching at low tip speeds: spilling the T

Using the criterion that a crack will extend along the direction of maximum circumferential stress, this paper demonstrates the influence of the coupling between the crack-parallel T-stress and the tip speed on the directional (in)stability of dynamics cracks in brittle materials, i.e., branching, turning, and limiting velocities. The proposed (in)stability criterion evolves within the theory of dynamic fracture: we build on the work of Ramulu and Kobayashi (1983) by introducing a reference distance ahead of the crack-tip to incorporate the contribution of the higher-order terms in the asymptotic solution of the elastic crack-tip fields. The theoretical aspect is first explored, a methodology to numerically (and experimentally) advocate the instability—as a co-action of T-stress and a fast-running crack—is then proposed and validated on Borden et al. (2012)’s branching benchmark. An experimental setup combining Ultra-High-Speed High-Resolution imaging with advanced Digital Image Correlation algorithms and a novel crack-branching inertial impact test enables for never-seen-before quantification of the rich dynamical behaviour of the fracture. This permits the experimental validation of the developed crack (in)stability criterion.

Unified digital image correlation under meshfree framework

AbstractThe implementation of digital image correlation (DIC) involves several different problems, such as image prefiltering, image intensity gradient calculation, image intensity interpolation at subpixel positions, shape function construction and strain calculation. This paper offers a unified insight into the nature of several key problems in DIC technique. We treat all the problems involved in the former mentioned key steps as fundamentally the same problem, that is, reconstructing an analytical description from a discrete and noisy sampling of a signal, such as discrete image intensity and displacement at some scattered nodes. From the reconstructed analytical description, the gradient and physical value at subpixel or integral pixel position can be analytically calculated without extra error. Here, we solve all these problems using the same mathematical tool, the meshfree method, leading to a unified DIC (U‐DIC) method. This method introduces errors solely during the steps involving the determination of the continuous description. However, it effectively avoids errors in the remaining steps. It holds the best balance between spatial resolution and measurement resolution for both displacement and strain measurements compared to 28 state‐of‐the‐art DIC algorithms based on the benchmark tests on DIC Challenge 2.0. It also holds all the unique advantages of the advanced meshfree DIC (MF‐DIC) compared to conventional local DIC and global DIC. The novel concept and excellent balance between spatial resolution and measurement resolution make U‐DIC an attractive replacement for conventional DIC methods. Additionally, the consistency of the implementation procedures in U‐DIC can simplify the parameter selection in DIC, which is of potential for the standardization of DIC technique.

Use of lightweight FFT-based algorithms for remote measurements using DIC

Abrupt collapse events that result in casualties due to failure of load-bearing structures have been reported in recent years. Continuous remote monitoring of these structures, assessing the behavior under operational load, could provide a reliable prevention, as the developing damage manifests itself through a loss of stiffness, and therefore excessive deflections. Digital image correlation (DIC) algorithms based on fast Fourier transform (FFT) were implemented in a small computer unit Raspberry Pi, capable of remote operation in a low-energy demand mode. The unit is fully autonomous, capable of localizing QR code targets and extracting encoded information. This strategy allows us to instantly evaluate displacements of QR codes attached to any surface and send the data to a server. The software was developed in Python, allowing for two modes: the online mode for current data evaluation with information transfer via the MQTT protocol and the offline mode used for retrospective evaluation.

Non-contact type dynamic responses test of wind turbines

The performance analysis of wind turbine systems should be considered when calculating the wind speed relative to the wind turbine structure, and is essential for wind turbine design. Since the conditions are precarious in transient state and the operating environments are challenging, the wind turbine is a complex, multivariate, nonlinear system. This paper presents a novel dynamic response test method for wind turbines based on three-dimensional digital speckle measurement. This method use a real-time speckle image collection of objects in various stages using binocular stereo vision to perform stereo matching of deformation points on object surface. A digital image correlation algorithm is used to rebuild three-dimensional space coordinates of matching points so as to achieve wind turbine dynamic response. A laboratory-scale experimental platform is constructed to test the dynamic response of the wind turbine system. In order to verify the accuracy of the proposed method, a three-dimensional model of a wind turbine is built. With dynamic structure response process adopted to carry out dynamic analysis and compare theoretical results with test results, the results vary by less than 10 %, indicating that the test method presented in the paper is feasible and effective.

Challenges and solutions of environmental scanning electron microscopy characterisation of biomaterials: Application to hygro‐expansion of paper

AbstractMost methodologies to measure the moisture‐induced deformation (hygro‐expansion) of paper microconstituents, including fibres and interfibre bonds, are low resolution or time‐consuming. Hence, here, a novel method is proposed and validated to measure high‐resolution full‐field strain maps of paper microconstituents during hygro‐expansion, based on environmental scanning electron microscopy (ESEM). To this end, a novel climate stage enables accurate control of the relative humidity (RH) near the specimen in the ESEM from 0%–100%. The fibre surface, which is decorateda prioriwith a microparticle pattern, is captured during RH change. Subsequently, correlating the fibre surface using a dedicated global digital image correlation algorithm enables high‐resolution hygro‐expansion strain maps. Method optimisation involved performing contrast enhancement, scan‐correction to reduce ESEM artefacts and a background correction, resulting in a strain resolution of . Method validation revealed that the fibres' crystallinity is affected by the electron beam, even for minimal invasive electron beam settings. Interestingly, however, the fibres consistently exhibit conventional hygro‐expansion behaviour during the drying slopes. Using the optimised procedure, hygro‐expansion characterisation of two interfibre bonds and four interfibre bond cross‐sections revealed the competition between the low longitudinal and large transverse fibre hygro‐expansion in the bonded area.

Detecting Recent Dynamics in Large-Scale Landslides via the Digital Image Correlation of Airborne Optic and LiDAR Datasets: Test Sites in South Tyrol (Italy)

Large-scale slow-moving deep-seated landslides are complex and potentially highly damaging phenomena. The detection of their dynamics in terms of displacement rate distribution is therefore a key point to achieve a better understanding of their behavior and support risk management. Due to their large dimensions, ranging from 1.5 to almost 4 km2, in situ monitoring is generally integrated using satellite and airborne remote sensing techniques. In the framework of the EFRE-FESR SoLoMon project, three test-sites located in the Autonomous Province of Bolzano (Italy) were selected for testing the possibility of retrieving significant slope displacement data from the analysis of multi-temporal airborne optic and light detection and ranging (LiDAR) surveys with digital image correlation (DIC) algorithms such as normalized cross-correlation (NCC) and phase correlation (PC). The test-sites were selected for a number of reasons: they are relevant in terms of hazard and risk; they are representative of different type of slope movements (earth-slides, deep seated gravitational slope Deformation and rockslides), and different rates of displacement (from few cm/years to some m/years); and they have been mapped and monitored with ground-based systems for many years (DIC results can be validated both qualitatively and quantitatively). Specifically, NCC and PC algorithms were applied to high-resolution (5 to 25 cm/px) airborne optic and LiDAR-derived datasets (such as hillshade and slope maps computed from digital terrain models) acquired during the 2019–2021 period. Qualitative and quantitative validation was performed based on periodic GNSS surveys as well as on manual homologous point tracking. The displacement maps highlight that both DIC algorithms succeed in identifying and quantifying slope movements of multi-pixel magnitude in non-densely vegetated areas, while they struggle to quantify displacement patterns in areas characterized by movements of sub-pixel magnitude, especially if densely vegetated. Nonetheless, in all three landslides, they proved to be able to differentiate stable and active parts at the slope scale, thus representing a useful integration of punctual ground-based monitoring systems.

A stable and effective calibration method for defocused cameras using synthetic speckle patterns

Well-focused and sharp target images are essential in traditional calibration, but they generally cause difficulty and inconvenience in image acquisition in practical applications. To address this problem, a stable and effective calibration method for defocused cameras is proposed in this study. Firstly, two speckle patterns are created, one of which has control points to estimate the initial value while the other, used for point pair match registration, is without control points. They are subsequently displayed on a fixed monitor and captured at each camera pose. Secondly, the initial guess for the control points is estimated using the proposed method. The retrieval of query points can then be done with high accuracy using the digital image correlation algorithm. Finally, all the calibration parameters, including lens distortion, are calculated using the plane calibration algorithm. The experiment in this study demonstrates the practicality and effectiveness of the proposed method. The accuracy and precision were quantitatively evaluated by the mean reprojection error, which was 0.057 pixels.

A Comparative Study of Structural Deformation Test Based on Edge Detection and Digital Image Correlation.

Digital image-correlation (DIC) algorithms rely heavily on the accuracy of the initial values provided by whole-pixel search algorithms for structural displacement monitoring. When the measured displacement is too large or exceeds the search domain, the calculation time and memory consumption of the DIC algorithm will increase greatly, and even fail to obtain the correct result. The paper introduced two edge-detection algorithms, Canny and Zernike moments in digital image-processing (DIP) technology, to perform geometric fitting and sub-pixel positioning on the specific pattern target pasted on the measurement position, and to obtain the structural displacement according to the change of the target position before and after deformation. This paper compared the difference between edge detection and DIC in accuracy and calculation speed through numerical simulation, laboratory, and field tests. The study demonstrated that the structural displacement test based on edge detection is slightly inferior to the DIC algorithm in terms of accuracy and stability. As the search domain of the DIC algorithm becomes larger, its calculation speed decreases sharply, and is obviously slower than the Canny and Zernike moment algorithms.

Full-Field Vibration Measurements by Using High-Speed Two-Dimensional Digital Image Correlation

This work developed a method that uses a single monochrome high-speed camera without sacrificing the spatial resolution to measure both in-plane and out-of-plane full-field vibrations. By using the high-speed camera and a two-dimensional digital image correlation (2D-DIC) algorithm, the method first extracts the out-of-plane displacement field from the measured virtual in-plane strains. Then it retrieves the in-plane displacement field after eliminating the out-of-plane motion-induced virtual component. For validation, in-plane and out-of-plane translation tests and single-frequency vibration experiments were carried out. The measurement results show good agreement with the reference values, indicating the effectiveness of the proposed high-speed 2D-DIC (HS-2D-DIC). Further, the natural frequencies and mode shapes of a rectangular cantilever panel were also measured successfully, exhibiting the method’s effectiveness in practical applications. Since the HS-2D-DIC requires only a single monochrome camera, no complex optical setup, and no complicated calibration process, the method can be developed as a competitive tool for full-field vibration characterizations.

Digital image correlation after focused ion beam micro-slit drilling: A new technique for measuring residual stresses in hardmetal components at local scale

A new method has been developed for measuring residual stresses at the surface of hardmetal components with higher spatial resolution than standard X-ray diffraction methods. It is based on measuring the surface displacements produced when stresses are partially released by machining a thin slit perpendicularly to the tested surface. Slit machining is carried out by focused ion beam (FIB). Measurement of the displacement fields around the FIB slit are performed by applying an advanced digital image correlation algorithm based on Fourier analysis with sub-pixel resolution. This method compares SEM images of the same area of the hardmetal surface before and after slitting. The method has been successfully applied to as-ground and femto-laser textured surfaces showing good correlation with the standard sin2ψ XRD technique. It is concluded that texturing induced by laser pulses in the femtoseconds regime is not perfectly adiabatic, since residual stresses are reduced by 15%.

Neutralizing the impact of heat haze on digital image correlation measurements via deep learning

Digital image correlation (DIC) techniques have shown excellent capabilities in the deformation measurements of materials and structures at high temperatures. However, as a crucial challenge for the high-temperature DIC measurements, heat haze, caused by uneven distributions of temperatures and refractive indices, can lead to considerable distortions to the captured images and severely decrease the measurement accuracy. Inspired by a recently established atmospheric turbulence neutralization neural network called TSR-WGAN, we propose a deep learning-based approach to neutralize the effect of heat haze on high-temperature DIC measurements. Specifically, the original distorted speckle images obtained in the experiment are fed into the TSR-WGAN network twice to obtain distortion-corrected speckle images. By processing these corrected speckle images with a conventional DIC algorithm, displacement and strain fields with mitigated heat haze effects can be determined. Both simulation and real experiments have been conducted to assess the proposed method. Results clearly show the effectiveness and robustness of the proposed method in correcting heat haze effects. One of the experiments particularly shows that the displacement fluctuations caused by the heat haze can be reduced by approximately an order of magnitude in the u direction and by a factor of five in the v direction. The proposed technique provides a much-needed tool for neutralizing the impact of heat haze on the DIC measurements under a high-temperature environment.

Measuring Local Tissue Strains in Tendons via Open-Source Digital Image Correlation.

There is considerable scientific interest in understanding the strains that tendon cells experience in situ and how these strains influence tissue remodeling. Based on this interest, several analytical techniques have been developed to measure local tissue strains within tendon explants during loading. However, in several cases, the accuracy and sensitivity of these techniques have not been reported, and none of the algorithms are publicly available. This has made it difficult for the more widespread measurement of local tissue strains in tendon explants. Therefore, the objective of this paper was to create a validated analysis tool for measuring local tissue strains in tendon explants that is readily available and easy to use. Specifically, a publicly available augmented-Lagrangian digital image correlation (ALDIC) algorithm was adapted for measuring 2D strains by tracking the displacements of cell nuclei within mouse Achilles tendons under uniaxial tension. Additionally, the accuracy of the calculated strains was validated by analyzing digitally transformed images, as well as by comparing the strains with values determined from an independent technique (i.e., photobleached lines). Finally, a technique was incorporated into the algorithm to reconstruct the reference image using the calculated displacement field, which can be used to assess the accuracy of the algorithm in the absence of known strain values or a secondary measurement technique. The algorithm is capable of measuring strains up to 0.1 with an accuracy of 0.00015. The technique for comparing a reconstructed reference image with the actual reference image successfully identified samples that had erroneous data and indicated that, in samples with good data, approximately 85% of the displacement field was accurate. Finally, the strains measured in mouse Achilles tendons were consistent with the prior literature. Therefore, this algorithm is a highly useful and adaptable tool for accurately measuring local tissue strains in tendons.

An unsupervised learning algorithm for computer vision-based blind modal parameters identification of output-only structures from video measurements

Operational modal analysis (OMA) is required to maintain large-scale and necessary civil infrastructures. The non-contact method of OMA using digital image correlation and point tracking algorithms requires a speckle pattern placed on the structure. Alternatively, advanced computer vision methods like optical flow and phase-based video motion magnification (PBVMM) techniques are used to measure modal parameters. Despite the importance of PBVMM, the users should know the range of frequencies in which the natural structure frequency lies. A methodology based on an unsupervised machine learning technique is developed to extract the modal parameters blindly from its recorded digital video. The proposed methodology uses complex steerable pyramids and an unsupervised machine learning technique, also known as principal component analysis, and analytical mode decomposition with a random decrement technique to blindly extract the modal parameters of a structure. This study validated the proposed methodology using a multi-degree of freedom (DOF) numerical model. The results are compared with theoretical and estimated values and are in good agreement. Furthermore, it is implemented on a laboratory benchmark SDOF, MDOF, and real-time videos of the London Millennium and Tacoma Narrows bridges for blindly extracting the modal frequencies and damping ratios.

Displacement Measurement Based on UAV Images Using SURF-Enhanced Camera Calibration Algorithm

Displacement is an important parameter in the assessment of the integrity of infrastructure; thus, its measurement is required in a multitude of guidelines or codes for structural health monitoring in most countries. To develop a low-cost and remote displacement measurement technique, a novel method based on an unmanned aerial vehicle (UAV) and digital image correlation (DIC) is presented in this study. First, an auxiliary reference image that meets the requirements is fabricated using the selected first image. Then, the speeded-up robust features (SURF) algorithm is introduced to track the feature points in the fixed areas. The least square algorithm is then employed to resolve the homography matrix of the auxiliary reference image and target images; then, the acquired homography matrices are utilized to calibrate the deviation caused by the UAV wobble. Finally, the integral pixel and sub-pixel matching of the DIC algorithm is employed to calculate the displacement of the target object. The numerical simulation results show that the proposed method has higher calculation accuracy and stability. The outdoor experiment results show that the proposed method has definite practicability.

Deformation measurement within lithium-ion battery using sparse-view computed tomography and digital image correlation

Electrode deformation can cause high local strain and serious capacity degradation in lithium-ion batteries (LIBs) during cycling. Risk reduction in many applications requires an understanding of the effects of the charging/discharging rate on the electrode structure during the battery life cycle. Cyclic charging/discharging experiments of wound 18 650 cylindrical LIBs were conducted at different charging/discharging rates (1C and 2C) to determine the effect of rate on electrode deformation. The charging/discharging capacity as well as battery voltage and time were analyzed during cycling. To acquire electrode deformation images and meet the requirements of computed tomography (CT) within 2 min during the charging/discharging process, sparse-view CT was performed at fixed cycle intervals. Subsequently, a sparse-view reconstruction network was proposed to generate a slice image. Finally, the electrode displacement and strain fields were calculated using the augmented Lagrangian digital image correlation algorithm. The causes of electrode deformation were analyzed and discussed from the perspective of molecular and macroscopic structure. Experimental results show that the structural similarity, peak signal-to-noise ratio and root mean square error in the reconstructed image of the axial section within the battery obtained via the proposed network were 0.9616, 38.7411 dB and 0.0108, respectively, which were better than the other methods of comparison. After 100 cycles, the capacity decay of the battery at 2C was 9.23-fold higher than that at 1C. After 100 cycles at 2C, the maximum displacement of the electrode reached 0.46 mm along the x-direction. The electrode structural deformation of the battery can be intuitively understood at different rates, which facilitates reasonable utilization and structural optimization of the battery.

Damage Detection for Rotating Blades Using Digital Image Correlation with an AC-SURF Matching Algorithm.

The motion information of blades is a key reflection of the operation state of an entire wind turbine unit. However, the special structure and operation characteristics of rotating blades have become critical obstacles for existing contact vibration monitoring technologies. Digital image correlation performs powerfully in non-contact, full-field measurements, and has increasingly become a popular method for solving the problem of rotating blade monitoring. Aiming at the problem of large-scale rotation matching for blades, this paper proposes a modified speeded-up robust features (SURF)-enhanced digital image correlation algorithm to extract the full-field deformation of blades. Combining an angle compensation (AC) strategy, the AC-SURF algorithm is developed to estimate the rotation angle. Then, an iterative process is presented to calculate the accurate rotation displacement. Subsequently, with reference to the initial state of rotation, the relative strain distribution caused by flaws is determined. Finally, the sensitivity of the strain is validated by comparing the three damage indicators including unbalanced rotational displacement, frequency change, and surface strain field. The performance of the proposed algorithm is verified by laboratory tests of blade damage detection and wind turbine model deformation monitoring. The study demonstrated that the proposed method provides an effective and robust solution for the operation status monitoring and damage detection of wind turbine blades. Furthermore, the strain-based damage detection algorithm is more advantageous in identifying cracks on rotating blades than one based on fluctuated displacement or frequency change.

Quantifying Population Movement Using a Novel Implementation of Digital Image Correlation in the ICvectorfields Package

Movements in imagery captivate the human eye and imagination. They are also of interest in variety of scientific disciplines that study spatiotemporal dynamics. Popular methods for quantifying movement in imagery include particle image velocimetry and digital image correlation. Both methods are widely applied in engineering and materials science, but less applied in other disciplines. This paper describes an implementation of a basic digital image correlation algorithm in R as well as an extension designed to quantify persistent movement velocities in sequences of three or more images. Algorithms are applied in the novel arena of landscape ecology to quantify population movement and to produce vector fields for easy visualization of complex movement patterns across space. Functions to facilitate analyses are available in ICvectorfields \citep{ICvf}. These methods and functions are likely to produce novel insights in theoretical and landscape ecology because they facilitate visualization and comparison of theoretical and observed data in complex and heterogeneous environments.

Digital image correlation based on convolutional neural networks

As an indispensable non-destructive testing technique, digital image correlation (DIC) has been increasingly applied to various engineering areas concerning deformation characterization. Inspired by artificial intelligence-related technologies, we here develop a new convolutional neural network-based theoretical framework for DIC analyses, hereafter called DIC-Net. A pyramidal structure is designed to ensure robustness and reliability of measurement results. Simultaneously, the second-order shape function is adopted to create training dataset, making the DIC-Net more suitable for solving complex deformation fields. Different from conventional DIC algorithms, the developed DIC-Net does not require specific correlation criterion, nor is it necessary to perform numerical iterative computations, which greatly enhances the efficiency of correlation calculations. The proposed DIC-Net not only provides an alternative approach to achieve accurate, precise and reliable deformation measurements, but also paves the way for developing high-efficiency DIC with real-time processing capabilities.

A non-contact approach to measure J-R curve for side-grooved specimens using digital image correlation

This manuscript presents a non-contact approach to measure the J-integral resistance (J-R) curve for side-grooved specimens using data measured solely from the digital image correlation (DIC). The DIC-based J measurement deploys specified contours and domains on the specimen free surface while the amount of crack extension derives from the rotation factor. The numerical analyses verify the DIC algorithm and investigate the side-grooving effects. This study establishes a fundamental relationship between the free-surface and through-thickness averaged J values, which is evidenced through theoretical and experimental validations. The modified free-surface J-integral achieves good agreement with the through-thickness averaged J results under small-scale yielding conditions. This study demonstrates that side grooves impose trivial effects on the accuracy of the DIC J-R curve measurement.

Background-Oriented Schlieren technique using Fast Checkerboard Demodulation for extending measurement range of pressure of underwater shock waves

We have successfully extended the measurement range by applying Fast Checkerboard Demodulation (FCD) to Background-Oriented Schlieren (BOS) technique, which is a contactless measurement technique. In the experiment, the apparent displacement field on the background image distorted by underwater shock waves were measured using FCD-BOS and contact measurement pressure (hydrophone). The results show that the measurement limitation of FCD is higher than the methods of previous studies which used Optical Flow (OF) and Digital Image Correlation (DIC) algorithms. Since the displacement is detected from the frequency change due to the distortion of the background image, FCD that combines fast Fourier transform (FFT) and a background image with a two-dimensional periodic pattern, can be applied to the large distortion which is difficult to be detected using OF and DIC. The numerical approach in our previous study(Shimazaki et al., 2022) showed that the measurement range of FCD-BOS is strongly influenced by the background pattern. Therefore, this study also performed numerical investigation on the use of synthetic background images with the frequency of the resultant wave of two sine waves of different wavelengths for FCD-BOS. The numerical results show that the measurement range using the synthetic background images with wavelengths of (60,60/12) μm is three times larger than the experimental measurement range using the lattice grid (width: 20 μm) background. In addition to the synthetic background of wavelengths of 60,60/12, the measurement accuracy, phase wrapping, overlap, and aliasing artifacts of those of (40,40/12) and (30,30/12) were investigated. Such results show the superiority of the background with the frequency of sine wave over that of square wave (lattice grid).