Digital Image Correlation (DIC) is a non-contact measurement technique for deformation with a long-studied challenge to find a balance between calculation efficiency and seed point quantity. Deep learning offers a new solution to improve DIC efficiency, and supervised learning DIC methods require high-quality training data, leading to challenges in ground-truth generation that can be time-consuming. We propose a DIC method for 2D displacement measurement based on unsupervised Convolutional Neural Network (CNN) to address the problem. A speckle image warp model is used to transform the target speckle image to the predicted reference speckle image according to the predicted 2D displacement map. The predicted and original reference speckle images are compared to achieve unsupervised training. Our proposed method eliminates the need for extensive training data annotation. We conducted several experiments to demonstrate its validity and robustness. The MAE and RMSE by unsupervised learning are only 0.0681 pixels and 0.0886 pixels, respectively, demonstrating the potential of our method to achieve accuracy that is comparable to supervised methods.
In this paper, we propose a lightweight deep convolution neural network, named PEENet, for high resolution image phase unwrapping in fringe projection profilometry on the device with limited performance. In our method, the dilated convolution strategy is applied to the networks, which increases the receptive field of the network while reducing the amount of network parameters. In the PEENet, we use Atrous Spatial Pyramid Pooling (ASPP) structure which can reduce the network parameters (total 0.48 million) while can extract the deep features of image. We also use Edge-Enhanced Block (EEB) structure, which can enhance the edge features of the image. We conducted ablation experiments to explore the effect of different network structures on network performance and then we compare our method with the other lightweight deep convolution neural network with the same training and testing datasets. We also build a new dataset that contain more different situations which can enhance the generalization ability of the network. The results show that our method achieves higher accuracy with fewer parameters and the new dataset works well.
Fringe projection profilometry has been widely applied to three-dimensional measurement. However, the nonlinear effect of the projector leads to errors in the unwrapped phase in the phase-shift method. In this paper, we propose a direct gamma estimation method. Theoretical derivation shows that the gamma factor is related to the three-step phase-shifted fringe patterns and the ideal unwrapped phase. The unwrapped phase after Gaussian low-pass filtering is taken as the initial estimate of the ideal unwrapped phase. We correct those abnormal values after calculating the gamma factor. The corrected gamma factor is used to inverse gamma correct the captured fringe patterns, and then the gamma-corrected unwrapped phase is obtained by phase demodulation and phase untangling from the inverse gamma corrected fringe patterns. Then we perform iterative operations on the gamma factor and ideal unwrapped phase. We consider the gamma-corrected unwrapped phase as the new ideal unwrapped phase, recalculate and update the new gamma factor until the gamma factor converges to a stable, desired state. Our method only needs to project and collect three frames of fringe pattern, which meets the high-speed measurements requirement. The experimental result of the face mask demonstrates that our method can effectively reduce the nonlinear phase errors.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.