For the digital image correlation (DIC) method, the measurement of specimens with complex shapes may encounter difficulties due to the time-consuming recognition of region of interest (ROI), and the indeterminate parameter selection caused by the non-uniform deformation. This paper proposes an automatic DIC for the measurement of structures with complex shapes. An automatic ROI segmentation is developed by combining a convolutional neural network (CNN) and image morphology, so the boundary of the specimen can be acquired accurately and efficiently. In dealing with the non-uniform deformation, a strain-related automatic selection of DIC parameters is developed, in which the sampling intervals and the subset sizes at different areas can be automatically determined. Both results of the simulated experiment and real experiment show that, by combing the two approaches with segmentation-aided digital image correlation, the proposed automatic DIC can characterize the complex deformation including the boundary of the structures effectively.

The use of supervised neural networks is a new approach to solving digital image correlation (DIC) problems, but the existing methods solely adopt the black-box neural network, i.e., the mapping from speckle image pair (reference image and deformed image) to multiple deformation fields (displacement fields and strain fields) is directly established without considering the physical constraints between the fields, causing a low level of accuracy that is even inferior to that of Subset-DIC. In this work, we proposed a deep learning model by introducing strain-displacement relations into a neural network, in which the effect of errors both in displacement and strain are considered in the network training. The back-propagation process of the proposed model is derived, and the solution scheme is implemented by Python. The performance of the proposed model is evaluated by simulation and real DIC experiments, and the results show that adding physical constraints to the neural network can significantly improve prediction accuracy.