Comparison of crack segmentation using digital image correlation measurements and deep learning

Reliable methods for detecting pixels that represent cracks from laboratory images taken for digital image correlation (DIC) are required for two main reasons. Firstly, the segmented crack maps are used as an input for some DIC methods that are based on discontinuous fields. Secondly, detected crack patterns can serve as inputs for predictive empirical models to obtain the level of damage to a body. The aim of this paper is to compare the performance of two approaches for crack segmentation on grayscale images acquired from two experimental campaigns on stone masonry walls. In the first approach, a threshold is applied to the maximum principal strain map calculated using post-processed DIC results. In the second approach, a deep convolutional neural network is used. The two methods are compared in terms of standard segmentation criteria, namely precision, dice coefficient and sensitivity. It is shown that the precision and dice coefficient obtained from the deep learning approach are much higher than those obtained from the threshold method (by almost 47% and 34%, respectively). However, the sensitivity computed from the deep learning method is slightly (~4%) lower than the threshold method. These results show that the deep learning method can better preserve the geometry of detected crack patterns, and the prediction in terms of pixels belonging to a crack is finally more accurate than the threshold method.