Towards enhancing AI-based crack segmentation for masonry surfaces through 3D data set synthesis

Automated crack detection and segmentation for concrete and masonry surfaces has been a field of interest for many years now, as it can provide many benefits like early detection of structural damage without the need for manual inspection. This task has traditionally been tackled manually or by using image processing techniques, but over the last few years neural networks have shown to be more effective (Deng et al., 2022). The downside to this approach, however, is that neural networks typically rely on large volumes of high quality and diverse data for model training. This data is often expensive and time-consuming to collect and inconsistent due to the manual annotation required. Especially for tasks like crack segmentation on masonry surfaces, which are more complex than the standard concrete segmentation, this becomes a problem (Özgenel et al., 2018).