## 4 Case Study
### Result Analysis and Discussion  
Table 6 summarizes the performance of the proposed model. The bold numbers indicate larger values than others. The types with the largest errors for each inspection item are Type 8 for the ribbon width and adhesive area, Type 5 for the dab distance (centroid location), and Type 4 for the dab diameter. Figure 12: Result of adhesive area. \begin{table}
\begin{tabular}{c c c c} \hline
**Type** & **Ribbon** & **Dab** & **Total** \\ \hline Type 1 & 9.00\% & 2.29\% & 6.12\% \\ Type 2 & 7.24\% & 3.78\% & 5.81\% \\ Type 3 & 8.85\% & 7.61\% & 8.34\% \\ Type 4 & 8.47\% & 0.95\% & 5.39\% \\ Type 5 & 1.56\% & 1.02\% & 1.23\% \\ Type 6 & 6.21\% & 3.98\% & 5.35\% \\ Type 7 & 9.91\% & 3.45\% & 7.14\% \\ Type 8 & 15.33\% & 4.33\% & 10.37\% \\ Type 9 & 11.37\% & 3.92\% & 8.01\% \\ \hline Total & 8.66\% & 3.48\% & 6.42\% \\ \hline \end{tabular}
\end{table} 
Table 5: Error of adhesive area. The analysis of the error of the proposed model revealed the following. First, the segmentation of thin ribbons was not performed properly. Type 8 and Type 9, which had the lowest accuracy in ribbon width and adhesive area measurements, were samples with ribbon thicknesses that differed significantly from other EIFS samples. Figure 12(a) shows the segmentation result of Type 8, while Figure 12(b) shows the result of Type 9. In Figure 12(a), the ribbon at the bottom was detected as larger than the insulation size, causing errors during the image transformation process. Second, the angle at which the insulation is photographed, combined with variations in adhesive thickness, significantly affects the accuracy of the model. Heavily tilted photos, such as those shown in Figure 14, result in distortions during the image transformation process, particularly for objects farther away from the camera. These distortions directly contribute to measurement errors. As shown in Figures 13(b) and 15, which depict the same EIFS image, it is evident that heavily tilted images experience distortion during the image transformation process. This distortion ultimately impacts the accuracy of the EIFS inspection results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Ribbon Width** & **Dab Distance** & **Dab Diameter** & **Adhesive Area** \\  & **(Ratio)** & **(RMSE)** & **(Ratio)** & **(Ratio)** \\ \hline Type 1 & 7.55\% & **12.95 mm** & 4.85\% & 6.12\% \\ Type 2 & 6.94\% & 4.13 mm & 4.81\% & 5.81\% \\ Type 3 & 5.70\% & 4.10 mm & 3.93\% & 8.34\% \\ Type 4 & 5.77\% & 6.16 mm & **7.72\%** & 5.39\% \\ Type 5 & 9.29\% & **14.64 mm** & 4.48\% & 1.23\% \\ Type 6 & 6.83\% & 3.95 mm & 5.23\% & 5.35\% \\ Type 7 & 7.97\% & 4.76 mm & 4.00\% & 7.14\% \\ Type 8 & **22.58\%** & 7.17 mm & 3.61\% & **10.37\%** \\ Type 9 & **12.11\%** & 4.20 mm & 4.39\% & 8.01\% \\ \hline Total & 9.42\% & 6.90 mm & 4.78\% & 6.42\% \\ \hline \hline \end{tabular} The bolded values indicate relatively higher errors.  
\end{table} 
Table 6: Summary of the proposed model’s performance. Figure 13: Examples of thin ribbon (**a**) segmentation of Type 8 (**b**) segmentation of Type 9. To ensure high performance when using the proposed model in the field, it is necessary to consider the following. First, when photographing insulation in the field, take pictures of the insulation from the front. This will reduce distortion from the camera lens and distortion due to adhesive thickness. Second, take pictures of the insulation from as close as possible. The larger the object in the image, the easier it is to extract the four vertices required for perspective transformation. This can lead to more accurate image alignment and, therefore, better measurement results. Finally, take photos with minimal shadows on the insulation. This will reduce the chances of incorrect segmentation results due to shadows. The proposed model can be used to determine the quality of the EIFS adhesive by setting a threshold value for each metric.