# 3D vision-based structural masonry damage detection

arXiv: https://arxiv.org/abs/2308.16380 · HTML: https://mlsanity.com/paper/2308.16380/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 50 references that are in the map.
- First posted on arXiv 31 Aug 2023.

## Builds on (most cited first, 5 of 5)

- [Open3D: A Modern Library for 3D Data Processing](https://mlsanity.com/paper/1801.09847/) (2018)
- [End-to-end Deep Learning Methods for Automated Damage Detection in Extreme Events at Various Scales](https://mlsanity.com/paper/2011.03098/) (2020)
- [Image-based monitoring of bolt loosening through deep-learning-based integrated detection and tracking](https://mlsanity.com/paper/2111.09117/) (2021)
- [Postdisaster image-based damage detection and repair cost estimation of reinforced concrete buildings using dual convolutional neural networks](https://mlsanity.com/paper/2111.09862/) (2021)
- [Automated building image extraction from 360deg panoramas for postdisaster evaluation](https://mlsanity.com/paper/1905.01524/) (2019)

## Abstract (arXiv)

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.