# Automated Multiple Concrete Damage Detection Using Instance Segmentation Deep Learning Model
## 4 Experimental Evaluation
### Evaluation Using Performance Measures
In Figure 8d, the spalling could not be completely detected owing to the blocking of a scaffold in the line of sight. The visual investigation inferred that despite the robust performance of the trained model, the performance can worsen owing to some unexpected objects with visual features similar to the damage and interruptions when capturing the entire damage shape. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Image No.** & \multicolumn{2}{c}{**Localization Purpose**} & \multicolumn{2}{c}{**Segmentation Purpose**} & **Remarks** \\  & **Precision** & **Recall** & **Precision** & **Recall** & \\ \hline Crack-1 & 93.75 & 100 & 89.97 & 92.37 & Figure 5a \\ Crack-2 & 100 & 100 & 91.34 & 87.88 & \\ Crack-3 & 97.82 & 79.16 & 86.28 & 81.81 & Figure 5b \\ Crack-4 & 100 & 100 & 87.97 & 90.94 & \\ Crack-5 & 100 & 100 & 89.02 & 75.15 & \\ Crack: average & 98.31 & 95.83 & 88.92 & 85.63 & \\ \hline Efflorescence-1 & 100 & 100 & 95.20 & 93.65 & \\ Efflorescence-2 & 100 & 100 & 93.91 & 88.73 & \\ Efflorescence-3 & 50 & 100 & 78.28 & 94.8 & Figure 6b \\ Efflorescence-4 & 50 & 100 & 89.33 & 98.83 & \\ Efflorescence-5 & 100 & 100 & 99.02 & 99.89 & Figure 6a \\ Efflorescence: average & 80 & 100 & 91.15 & 95.18 & \\ \hline Spalling/Rebar-1 & 100 & 100 & 96.16 & 99.44 & Figure 7a \\ Spalling/Rebar-2 & 95.23 & 77.78 & 87.51 & 89.36 & \\ Spalling/Rebar-3 & 100 & 100 & 94.66 & 91.11 & \\ Spalling/Rebar-4 & 100 & 100 & 96.38 & 82.82 & \\ Spalling/Rebar-5 & 66.67 & 100 & 65.56 & 83.77 & Figure 7b \\ Spalling/Rebar: average & 92.37 & 95.55 & 88.05 & 89.30 & \\ \hline Collocated damages-1 & 100 & 83.33 & 87.59 & 86.01 & \\ Collocated damages-2 & 100 & 66.67 & 88.44 & 68.92 & Figure 8d \\ Collocated damages-3 & 87.5 & 80 & 88.65 & 84.80 & \\ Collocated damages-4 & 85.71 & 100 & 67.67 & 88.93 & \\ Collocated damages-5 & 100 & 100 & 90.86 & 91.72 & Figure 8a \\ Collocated damages-6 & 75 & 85.71 & 74.84 & 79.48 & \\ Collocated damages-7 & 79.31 & 59.25 & 83.14 & 78.85 & Figure 8c \\ Collocated damages-8 & 100 & 83.33 & 92.80 & 77.70 & \\ Collocated damages-9 & 83.33 & 75 & 85.58 & 84.51 & \\ Collocated damages-10 & 96 & 80 & 89.47 & 89.74 & Figure 8b \\ Collocated damages: average & 86.73 & 76.66 & 85.16 & 82.06 & \\ \hline Overall average & 90.41 & 90.81 & 87.24 & 87.58 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Evaluation results for trained Mask R-CNN for multiple concrete damage detection. Figure 5: Examples of crack detection results (left column: original images with ground truth, right column: detection result of trained Mask R-CNN): (**a**) detection result of Crack-1 in Table 1, and (**b**) detection result of Crack-3 in Table 1.  
Figure 6: Examples of efflorescence detection results (left column: original images with ground truth, right column: detection result of trained Mask R-CNN): (**a**) detection result of Efflorescence-5 in Table 1, and (**b**)detection result of Efflorescence-3 in Table 1.  
Figure 8: \(Cont\). Figure 7: Examples of spalling and rebar exposure detection results (left column: original images with ground truth, right column: detection result of trained Mask R-CNN): (**a**) detection result of Spalling/Rebar-1 in Table 1, and (**b**) detection result of Spalling/Rebar-5 in Table 1.