# Automated 3D Image Processing System for Inspection of Residential Wall Spalls
## 3 Results
### Wall Spall Intelligent Recognition Experiment
#### 3.2.2 Testing Result
In RandLA-Spall, the dual-branch structure, consistency constraints, and CBAM modules contribute positively and significantly to enhancing the quality of feature representation and point cloud segmentation. Therefore, we meticulously evaluated the synergistic benefits of integrating these modules, demonstrating that holistic optimization of the \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Network** & **Randla-Net** & **Ours** \\ \hline \hline  & Point number & 4096*16 & 4096*16 \\  & mluu & 77.45 & 84.20 \\  & mFI & 85.61 & 90.71 \\  & mRecall & 90.27 & 94.97 \\ Recall & Spall & 82.16 & 90.40 \\  & Background & 99.28 & 99.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Segmentation refinement assessment indicators. Figure 14: Larger-scale structures semantic segmentation results. Figure 15: Comparison of segmentation indicators. proposed model leads to enhanced model effectiveness. To this end, a series of ablation experiments were conducted on the test set to analyze these critical components. First, the consistency constraints were removed from RandLA-Spall, resulting in a network called Model A. Second, the CBAM module was removed from RandLA-Spall, with the modified network called Model B. These models were trained under identical data, strategy, and hyperparameter settings. The results of the ablation experiments are displayed in Table 8.  
As illustrated in Table 8, removing the consistency constraint results in a 4.04% decrease in mIoU for Model A. This decrease occurs because Model A, lacking the consistency constraint, does not have a unified supervisory signal to manage feature perturbations of the two input branching modules during the learning process, resulting in inconsistent feature representations and degraded segmentation performance. This indicates that the dual-branch module broadens the scope and distribution of the training data, while the consistency constraint minimizes the prediction discrepancies between the two input modules, providing an additional supervisory signal that enhances the RandLA-Spall point cloud segmentation performance. Model B, which removes CBAM, experiences a 3.45% decrease in mIoU. Although there is an increase in training data enabling more feature representations, without CBAM's assistance to focus on key features, the accuracy of the segmentation boundaries is not fully optimized. This underscores the effectiveness of the CBAM attention module in focusing the model on more useful features and providing more precise guidance. Figure 16 visually presents the performance comparison of the ablation models. Figure 16: Comparison of ablation experiment indicators. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Network** & \
\begin{tabular}{c} **Twin-Branch** \\ **Architecture** \\ \end{tabular} &
\begin{tabular}{c} **Consistency** \\ **Constraints** \\ \end{tabular} & **CBAM** & **mIoU** & **mRecall** & **mF1** \\ \hline RandLA-net & & & & 77.45 & 90.72 & 85.61 \\ Module A & \(\surd\) & & \(\surd\) & 80.16 & 90.42 & 87.78 \\ Module B & \(\surd\) & \(\surd\) & & 80.75 & 91.95 & 87.20 \\ Ours & \(\surd\) & \(\surd\) & \(\surd\) & 84.20 & 94.97 & 90.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Segmentation metrics for ablation experiments.