## 5 Conclusions  
The limitations of existing research on automatic detection of bridge damage using DL technology are (1) the training image and (2) the shape of the target object. To address these limitations, a modeling framework based on a DL model with a two-step process was proposed as follows: (1) SR was used to enhance the image quality, thus ensuring diversity in the images of bridge damage and constructing a detection model that enabled the identification of damaged objects with complex structures. (2) Rather than performing detection as a single integrated model, DL models optimized for each member and each type \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Measure**} & \multicolumn{2}{c}{**Abutment/**} & \multirow{2}{*}{**Road Pavement**} & \multirow{2}{*}{**Slab Model**} & \multirow{2}{*}{**Rail Model**} & \multicolumn{2}{c}{**Model Average**} \\  & & & & & & & & & **by Member** \\ \cline{2-2} \cline{6-10}  & \multirow{2}{*}{**Blendmask**} & **Mask-** & \multirow{2}{*}{**Blendmask**} & **Mask-** & \multirow{2}{*}{**Blendmask**} & **Mask-** & \multirow{2}{*}{**Blendmask**} & **Mask-** & \multirow{2}{*}{**Blendmask**} & **Mask-** \\  & **RCNN** & & & & & & & & **RCNN** & & **RCNN** & \\ \hline AP50 & 92.871 & 100.00 & 98.020 & 91.412 & 100 & 98.613 & 94.257 & 100.00 & 96.96 & 97.506 \\ AP75 & 92.871 & 96.923 & 98.020 & 100.00 & 100 & 93.069 & 94.257 & 100.00 & 96.96 & 97.498 \\ Aps & 70.677 & 80.387 & 89.318 & 100.00 & 94.718 & 81.638 & 80.889 & 92.885 & 84.90 & 88.727 \\ Apm & 94.326 & 88.861 & 94.685 & 90.915 & 97.949 & 86.380 & 86.888 & 97.764 & 95.65 & 90.980 \\ Apl & 98.389 & 91.088 & 97.789 & 92.429 & 100 & 93.676 & 98.195 & 98.985 & 98.73 & 94.044 \\ mAP & 90.470 & 87.426 & 91.703 & 92.446 & 96.426 & 86.347 & 86.409 & 95.277 & 92.87 & 90.374 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Damaged-object-detection performance results for member specific models. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Measure** **(mAP)**} & \multirow{2}{*}{**Abutment/Pier Model**} & \multicolumn{2}{c}{**Road Pavement**} & \multirow{2}{*}{**Slab Model**} & \multirow{2}{*}{**Rail Model**} & \multirow{2}{*}{**Rail Model**} \\  & & & & & & & & **by Member** \\ \cline{2-2} \cline{6-10}  & \multirow{2}{*}{**Blendmask**} & **Mask-** & \multirow{2}{*}{**Blendmask**} & **Mask-** & \multirow{2}{*}{**Blendmask**} & **Mask-** & \multirow{2}{*}{**Blendmask**} & **Mask-** & \multirow{2}{*}{**Blendmask**} & **Mask-** \\  & **RCNN** & & & & & & & **RCNN** & & **RCNN** & \\ \hline Efflorescence & 90.614 & 88.875 & - & & 92.525 & 91.507 & 84.050 & 95.050 \\ Scaling & 98.064 & 88.239 & - & 94.863 & 91.740 & 89.894 & 100.00 \\ Spalling & 98.069 & 92.003 & - & 100 & 91.740 & 90.470 & 91.854 \\ Cracks & 100 & 90.000 & 91.703 & 91.412 & 98.317 & 71.386 & 69.802 & 94.208 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Damaged-object-detection performance results of member specific models by damage type. of damage were derived, which were finally aggregated. Thus, we proposed the BDODC-F, which encompassed all processes, namely data input, dataset construction, model training, detection, and output of results. As the shapes considerably differ with the type of damage for each member, the use of a single model for all members to perform training and detection would limit model performance. Therefore, in this model, we constructed individual models optimized for each member. Further, through these member-specific models, each type of damage was detected, and the results were aggregated. The experimental results demonstrated an improvement in detection performance with an increase in the mAP value of 110.6% over the existing model. However, as the model used an insufficient number of images, more images must be collected to reduce overfitting and improve the model's generalizability. Nevertheless, the performance of the combination model based on member-specific optimization is satisfactory.