**Marek Sło ´nski \* and Marcin Tekieli**

Faculty of Civil Engineering, Cracow University of Technology, ul. Warszawska 24, 31-155 Kraków, Poland; Marcin.Tekieli@pk.edu.pl

**\*** Correspondence: Marek.Slonski@pk.edu.pl; Tel.: +48-12-628-2562

Received: 30 June 2020; Accepted: 5 August 2020; Published: 10 August 2020

**Abstract:** This paper shows how 2D digital image correlation (2D DIC) and region-based convolutional neural network (R-CNN) can be combined for image-based automated monitoring and assessment of surface crack development of concrete structural elements during laboratory quasi-static tests. In the presented approach, the 2D DIC-based monitoring enables estimation of deformation fields on the surface of the concrete element and measurements of crack width. Moreover, the R-CNN model provides unmanned simultaneous detection and localization of multiple cracks in the images. The results show that the automatic monitoring and evaluation of crack development in concrete structural elements is possible with high accuracy and reliability.

**Keywords:** digital image correlation; region-based convolutional neural network; machine learning; crack monitoring; crack detection and localization
