Abstract
Background
Deep learning-based digital image correlation (DL-based DIC) has gained increasing attention in the last two years. However, existing DL-based DIC algorithms are impractical because their application scenarios are mostly limited to small deformations.
Objective
To enable the use of DL-based DIC in real-world general experimental mechanics scenarios that would involve large deformations and rotations, we propose to improve DL-based DIC with the domain decomposition method (DDM).
Methods
In the improved method, the region of interest is divided into subimages, and subimages are pre-aligned using the preregistered control points to effectively eliminate the large deformation components. The residual deformations in each subimage are small and limited, which can be well extracted using existing DL-based DIC methods.
Results
Through synthesized and real-world experiments, the improved DL-based DIC method can achieve high-accuracy pixelwise matching in practical applications with strong robustness and high computational efficiency.
Conclusions
The improved DL-based DIC combines the advantages of traditional and DL-based DIC methods but overcomes the limitations, greatly improving the robustness and applicability of existing DL-based methods.
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Data Availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Reference
Pan B, Qian K, Xie H, Asundi A (2009) Two-dimensional digital image correlation for in-plane displacement and strain measurement: a review. Measure Sci Technol 20:062001. https://doi.org/10.1088/0957-0233/20/6/062001
Pan B (2018) Digital image correlation for surface deformation measurement: historical developments, recent advances and future goals. Meas Sci Technol 29:082001. https://doi.org/10.1088/1361-6501/aac55b
Schreier H, Orteu J-J, Sutton MA (2009) Image Correlation for Shape, Motion and Deformation Measurements: Basic Concepts,Theory and Applications. Springer US, Boston, MA
Bruck HA, McNeill SR, Sutton MA, Peters WH (1989) Digital image correlation using Newton-Raphson method of partial differential correction. Exp Mechan 29:261–267. https://doi.org/10.1007/BF02321405
Lu H, Cary PD (2000) Deformation measurements by digital image correlation: Implementation of a second-order displacement gradient. Exp Mechan 40:393–400. https://doi.org/10.1007/BF02326485
Sun Y, Pang JH, Wong CK, Su F (2005) Finite element formulation for a digital image correlation method. Applied Optics 44:7357–7363. https://doi.org/10.1364/AO.44.007357
Besnard G, Hild F, Roux S (2006) “Finite-Element” Displacement Fields Analysis from Digital Images: Application to Portevin–Le Châtelier Bands. Exp Mech 46:789–803. https://doi.org/10.1007/s11340-006-9824-8
Ma S, Zhao Z, Wang X (2012) Mesh-based digital image correlation method using higher order isoparametric elements. J Strain Analys Eng Des 47:163–175. https://doi.org/10.1177/0309324712437488
Wittevrongel L, Lava P, Lomov SV, Debruyne D (2015) A Self Adaptive Global Digital Image Correlation Algorithm. Exp Mech 55:361–378. https://doi.org/10.1007/s11340-014-9946-3
Hornik K (1991) Approximation capabilities of multilayer feedforward networks. Neural Netw 4:251–257. https://doi.org/10.1016/0893-6080(91)90009-T
Boukhtache S, Abdelouahab K, Berry F et al (2021) When Deep Learning Meets Digital Image Correlation. Optics Lasers Eng 136:106308. https://doi.org/10.1016/j.optlaseng.2020.106308
Boukhtache S, Abdelouahab K, Bahou A et al (2023) A lightweight convolutional neural network as an alternative to DIC to measure in-plane displacement fields. Optics Lasers Eng 161:107367. https://doi.org/10.1016/j.optlaseng.2022.107367
Ronneberger O, Fischer P, Brox T (2015) U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Springer International Publishing, Cham, pp 234–241
Dosovitskiy A, Fischer P, Ilg E et al (2015) FlowNet: Learning Optical Flow with Convolutional Networks. In: 2015 IEEE International Conference on Computer Vision (ICCV). 2758–2766
Sun D, Yang X, Liu M-Y, Kautz J (2018) PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 8934–8943
Yang R, Li Y, Zeng D, Guo P (2022) Deep DIC: Deep learning-based digital image correlation for end-to-end displacement and strain measurement. J Mater Proc Technol 302:117474. https://doi.org/10.1016/j.jmatprotec.2021.117474
Wang Y, Zhao J (2023) DIC-Net: Upgrade the performance of traditional DIC with Hermite dataset and convolution neural network. Optics Lasers in Engineering 160:107278. https://doi.org/10.1016/j.optlaseng.2022.107278
Xiao H, Li C, Feng M (2023) Large Deformation Measurement Method of Speckle Images Based on Deep Learning. Acta Optica Sinica 43:1–13. https://doi.org/10.3788/AOS222084
Ma C, Ren Q, Zhao J (2021) Optical-numerical method based on a convolutional neural network for full-field subpixel displacement measurements. Opt Express 29:9137. https://doi.org/10.1364/OE.417413
Duan X, Huang J (2022) Deep learning-based digital volume correlation. Extreme Mechan Lett 53:101710. https://doi.org/10.1016/j.eml.2022.101710
Duan X, Huang J (2022) Deep-learning-based 3D cellular force reconstruction directly from volumetric images. Biophys J 121:2180–2192. https://doi.org/10.1016/j.bpj.2022.04.028
Duan X, Xu H, Dong R et al (2023) Digital image correlation based on convolutional neural networks. Optics Lasers Eng 160:107234. https://doi.org/10.1016/j.optlaseng.2022.107234
Yang J, Qian K, Wang L (2024) R3-DICnet: an end-to-end recursive residual refinement DIC network for larger deformation measurement. Opt Express 32:907. https://doi.org/10.1364/OE.505655
Hui T-W, Tang X, Loy CC (2018) LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 8981–8989
Teed Z, Deng J (2020) RAFT: Recurrent All-Pairs Field Transforms for Optical Flow. In: Vedaldi A, Bischof H, Brox T, Frahm J-M (eds) Computer Vision – ECCV 2020. Springer International Publishing, Cham, pp 402–419
Wang G, Zhang L, Yao X (2022) StrainNet-3D: Real-time and robust 3-dimensional speckle image correlation using deep learning. Optics Lasers Eng 158:107184. https://doi.org/10.1016/j.optlaseng.2022.107184
Toselli A, Widlund OB (2005) Domain decomposition methods–algorithms and theory. Springer, Berlin
Dolean V, Jolivet P, Nataf F (2015) An Introduction to Domain Decomposition Methods: Algorithms, Theory, and Parallel Implementation. Society for Industrial and Applied Mathematics, Philadelphia, PA
Passieux J-C, Périé J-N, Salaün M (2015) A dual domain decomposition method for finite element digital image correlation. Int J Numeric Meth Eng 102:1670–1682. https://doi.org/10.1002/nme.4868
Chi Y, Pan B (2022) Accelerating parallel digital image correlation computation with feature mesh interpolation. Measurement 199:111554. https://doi.org/10.1016/j.measurement.2022.111554
Schreier HW, Sutton MA (2002) Systematic errors in digital image correlation due to undermatched subset shape functions. Exp Mechan 42:303–310. https://doi.org/10.1007/BF02410987
Pan B, Xie H, Wang Z et al (2008) Study on subset size selection in digital image correlation for speckle patterns. Optics Exp 16:7037–7048
Rupil J, Roux S, Hild F, Vincent L (2011) Fatigue microcrack detection with digital image correlation. J Strain Analys Eng Design 46:492–509. https://doi.org/10.1177/0309324711402764
Hild F, Bouterf A, Roux S (2015) Damage measurements via DIC. Int J Fract 191:77–105. https://doi.org/10.1007/s10704-015-0004-7
Chi Y, Zhao W, Pan B (2023) Gray level residual field: an effective metric for pixelwise matching quality evaluation in local digital image correlation. Measure Sci Technol 34. https://doi.org/10.1088/1361-6501/accbdf
Acknowledgments
This work is supported by the National Natural Science Foundation of China (Grant nos. 11925202). National Science and Technology Major Project (J2019-V-0006-0099).
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Chi, Y., Liu, Y. & Pan, B. Improving Deep Learning-Based Digital Image Correlation with Domain Decomposition Method. Exp Mech 64, 575–586 (2024). https://doi.org/10.1007/s11340-024-01040-6
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DOI: https://doi.org/10.1007/s11340-024-01040-6