  The detection of masonry damage is essential for preventing potentially
disastrous outcomes. Manual inspection can, however, take a long time and be
hazardous to human inspectors. Automation of the inspection process using novel
computer vision and machine learning algorithms can be a more efficient and
safe solution to prevent further deterioration of the masonry structures. Most
existing 2D vision-based methods are limited to qualitative damage
classification, 2D localization, and in-plane quantification. In this study, we
present a 3D vision-based methodology for accurate masonry damage detection,
which offers a more robust solution with a greater field of view, depth of
vision, and the ability to detect failures in complex environments. First,
images of the masonry specimens are collected to generate a 3D point cloud.
Second, 3D point clouds processing methods are developed to evaluate the
masonry damage. We demonstrate the effectiveness of our approach through
experiments on structural masonry components. Our experiments showed the
proposed system can effectively classify damage states and localize and
quantify critical damage features. The result showed the proposed method can
improve the level of autonomy during the inspection of masonry structures.
