A Method for Crack Detection and Quantification in Masonry Using Neural Network-Based Image Analysis

Abstract. This article presents a method for the automated detection and quantification of cracks on masonry surfaces. The core of the approach is a neural network trained for semantic segmentation, which enables the identification of cracks in image data. To facilitate a physically meaningful analysis, the image data is combined with 3D geometric information. A 3D point cloud is projected onto the image plane to establish correspondences between 2D image points and 3D spatial coordinates. These 2D–3D correspondences are utilized to evaluate the detected cracks in a geometrically accurate manner. Based on the segmentation results and the projected 3D data, cracks can be classified within the point cloud and analyzed metrically. The Crack length is determined using a graph-based model, in which the crack structure is represented as a network and the longest continuous crack path is computed using Dijkstra’s algorithm. The Crack width is measured in the images based on the segmentation masks and a scaling factor derived from the 2D–3D correspondences. The proposed method enables a precise and automated assessment of crack patterns in masonry structures by leveraging both image and 3D data.