# Automated 3D Image Processing System for Inspection of Residential Wall Spalls
## 4 Discussion and Conclusions  
Current wall spall detection using 2D images lacks spatial location information, limiting its ability to accurately characterize the location of visual defects. Point clouds, with their 3D spatial data, provide a viable alternative for spall detection. However, the vast number of aging civil buildings makes manual collection and detection of defects  
Figure 17: Sample example. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Date** & **Temperature (°C)** & **Cluster Number** & **Length (m)** & **Width (m)** \\ \hline
9 July & 25.9 & Cluster 0 & 0.39 & 0.64 \\
2024 & & Cluster 1 & 0.55 & 0.51 \\  & & Cluster 2 & 0.22 & 0.37 \\  & & Cluster 0 & 0.41 & 0.64 \\
7 February 2025 & \(-4\) & Cluster 1 & 0.57 & 0.52 \\  & & Cluster 2 & 0.26 & 0.37 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Geometric information of the samples. in 3D point clouds time-consuming and labor-intensive. This highlights the need for an automated 3D inspection method capable of efficiently collecting and segmenting wall spalls in civil buildings to support evaluation, inspection, and repair processes. This study contributes to addressing these challenges in three key aspects as follows:  
* First, given the inefficiency associated with manual collection and detection of spall diseases, data collection is integrated with intelligent robotics in this study. A SLAM-based path-planning UGV is proposed, providing a semi-automated solution for wall spall data collection, effectively improving data collection efficiency and reducing manual intervention. Through this integration and enhancement, a foundational hardware and software platform for subsequent research is established. The approach exhibits considerable application potential in the realm of building structural health monitoring, particularly within practical engineering contexts, where it can be broadly applied to large-scale structural inspections and long-term monitoring. * Second, in response to challenges associated with the point cloud dataset, namely small size and imbalance, a point cloud-based deep learning method has been developed--Randla-Spall. This semantic segmentation network model is tailored for detecting surface spall diseases on residential buildings under small sample conditions. Through modifications such as the inclusion of the dynamically enhanced dual-branch module in the network structure, integration of the CBAM attention mechanism, and optimization of the loss function, the model's robustness and accuracy have been significantly improved, particularly under data scarcity conditions. Compared to other classical segmentation models, this method achieves the highest mIoU (84.20%) and the highest spall recall (90.40%) in detecting concrete surface spalls from 3D point cloud data. These results demonstrate that the model can effectively segment point cloud data, providing an efficient solution framework for similar tasks, and also suggest that this approach holds significant potential for engineering applications. * Finally, the detected defects are quantified, and their key parameters are obtained so as to enhance the objectivity and accuracy of the assessment, complete the integrated process of data acquisition, disease segmentation, and spall assessment, and provide a scientific basis for future studies of defect detection on residential surfaces and building maintenance using quantitative results. Overall, a promising solution for the semi-automated detection and segmentation of surface spalls on residential walls is offered in this paper. However, despite these contributions, several outstanding issues that require further enhancement are recognized and should be addressed in future research as follows:  
* Although satisfactory accuracy is exhibited by the proposed model, it is limited by its dependence on high-performance servers and slow training and testing times.