Since traditional inspection methods of structures are time-consuming and prone to human errors, many researchers have investigated the possibility of using various deep learning models to automate damage detection and, in particular, crack detection. However, deep learning models require a large amount of training data to predict reasonably accurate results. Creating a dataset with segmented cracks is time-consuming, and the aim of this paper is, therefore, to present a semi-automated labelling process of cracks. This has the potential to greatly decrease the time spent creating datasets.