## 4 Experiments and Results
### Results of Image Quality Enhancement
#### 4.2.3 Experiment and Evaluation of the Model Performance Optimized by Member  
This study focused on six types of damage: efflorescence, concrete scaling, concrete spelling, cracks, water leak, and RC corrosion. However, excluding the objects that were not observed in all members, and those objects that were too few, we analyzed individual models by member for efflorescence, concrete scaling, and concrete spelling. However, the detection performance value (mAP, etc.) represents performance for all types. Models for the road pavement/abutment and pier/slab/rail were constructed to conduct performance testing. Fifty images of each member were used for training, 80 images for the aggregated model, 20 images for validation. For each member, 75,000 epochs were completed, and the performance was evaluated with the derived model for each member. Figure 10 shows the types of bridge members. To evaluate the improvement in performance after optimization of the respective member-specific models, an existing model trained with the damaged-object images of all members without differentiation by member was used for comparative performance assessment. First, the experimental results of the existing model are presented as follows. Figure 11 shows the result of object detection using the model trained for each type of bridge damage. Table 7 shows the performance results of the existing model. The existing model exhibited an accuracy of 92.675 (Blendmask), 98.679 (Mask-RCNN) based on AP 50, and 94.013 (Blendmask), 91.372 (Mask-RCNN) for a large object (Apl) and 76.414 (Blendmask), 88.051 (Mask-RCNN) for a small object (APs). The existing model also demonstrated a fair level of overall performance. The overall performance is slightly better with Blendmask. Figure 10: Type of bridge members. Table 8 presents the results of testing the detection performance of the member-specific models. The overall performance indices improved for each model, compared to the existing integrated model. As each member yielded different structures or shapes according to the type of damage, the results confirmed that the proposed model, which was trained separately on individual members followed by model optimization, displayed markedly better performance than the existing model. However, because of the small volume of collected image data, we observed overfitting of the results in a few types of damage. This demerit must be addressed by acquiring sufficient data. In terms of consideration of the performance indices for member-specific models, the value of mAP improved by 11.31 (Blendmask), 8.893 (Mask-RCNN) percent points over the existing model (all members integrated). \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Measure**} & \multicolumn{2}{c}{**Existing Model**} \\ \cline{2-3}  & **(Model without Detection Training by Member)** \\ \cline{2-3}  & **Blendmask** & **Mask-RCNN** \\ \hline AP50 & 92.675 & 98.679 \\ AP75 & 92.121 & 96.341 \\ eps & 76.414 & 79.006 \\ Apm & 92.508 & 88.051 \\ Apl & 94.013 & 91.372 \\ mAP & 83.965 & 83.977 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Damaged-object-detection performance results for existing models (all members integrated). Figure 11: Result image of bridge damaged-object type and detection. Table 9 illustrates the detection performance for member-specific models categorized into the four damage types. Overall, the model detected concrete scaling/spalling accurately. For the slab model, the mAP value was higher for the detection of efflorescence than for the detection of abutment/pier or rail, because the shape of efflorescence is more consistent across the images than other damage types. In the case of road pavement, cracks were the only type of damage, and this model cannot detect multiple damaged objects. The shape of the damaged object for each member is distinct. Application of the same model to all members is not an optimal detection approach.