# Automated 3D Image Processing System for Inspection of Residential Wall Spalls
## 3 Results
### Wall Spall Intelligent Recognition Experiment
#### 3.2.2 Testing Result  
To validate the effectiveness of the proposed model, a comparative analysis was performed on the test set using other state-of-the-art point cloud segmentation methods. Under identical experimental conditions, four models were evaluated. Table 6 shows the results of this comparison, illustrating the performance of the various methods. Notably, the proposed model surpassed the other point cloud segmentation methods. Since spall detection involves detecting small targets with a predominance of background point clouds, all models achieved good accuracy results. RandLA-Spall achieved the highest mIoU and mF1, at 84.20% and 90.71%, respectively, on the wall spall point cloud test set by sacrificing less testing time. Furthermore, compared to other models, the proposed model demonstrated a significant improvement of 5.37% in mIoU by adopting optimized strategies. This significant improvement strongly underscores the effectiveness of the optimization strategy in enhancing overall segmentation generalization performance. Figure 12 visually presents the performance comparison of the models. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Network** & **mIoU** & **mRecall** & **mF1** & **Acc** &
\begin{tabular}{c} **Testing** \\ **Time** \\ \end{tabular} & **Epoch** \\ \hline Pointnet++ [18] & 67.48 & 78.79 & 76.97 & 98.75 & 6 min 22 s & 100 \\ Randla-Net [21] & 77.45 & 90.72 & 85.61 & 99.04 & 3 min 06 s & 100 \\ BAF-LAC [39] & 78.83 & 88.52 & 86.83 & 99.17 & 3 min 49 s & 100 \\ Ours & 84.20 & 94.97 & 90.71 & 99.39 & 3 min 37 s & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Segmentation assessment indicators. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** &
\begin{tabular}{c} **Number of** \\ **Input Points** \\ \end{tabular} & **Learning Rate** & **mIoU** & **Epoch** \\ \hline A & 4096*10 & 0.01 & 80.95 & 100 \\ B & 4096*16 & 0.01 & 81.24 & 100 \\ C & 4096*10 & 0.001 & 83.98 & 100 \\ D & 4096*16 & 0.001 & 84.75 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Segmentation assessment indicators. Figure 13 shows the visualization results of ground truth, BAF-LAC, Randla-Net, and our method. Compared with the ground truth, Randla-Net shows more misdetections, omissions, and inaccurate detections. The diamond-shaped circled areas in Figure 13 highlight obvious misdetections; the oval-shaped circled areas in Figure 13 indicate clear instances of missed and inaccurate detections. A comparative analysis of Figure 13 demonstrates that RandLA-Spall offers the best results among all methods, achieving the optimal fit to the disease edge. To further validate the accuracy of the model on larger-scale structures, we conducted tests on a 10 m long corridor, as shown in Figure 14. The experimental results demonstrate that our method can still achieve good segmentation performance even in large-scale point clouds with a high background point cloud ratio, confirming its potential for application in large-scale structural inspections. This experimental validation highlights the robustness of  
Figure 12: Comparison of experimental indicators. Figure 13: Semantic segmentation results. our method, showing that it can be effectively extended to large-scale structural detection, making it suitable for real-world engineering applications, especially in environments with significant background noise and complex geometries. Considering the training results and the facility's performance capabilities, this paper selects 4096*16 points as the input and a learning rate (LR) of 0.001 and conducts a detailed analysis of the test results for the proposed method and baseline network under the same input conditions. Figure 15 provides an intuitive comparison, and Table 7 displays the segmentation metrics. Our model achieved a spall recall rate of 90.40% on the test set, demonstrating RandLA-Spall's superiority in spall segmentation.