## 4 Experiments and Results
### Results of Image Quality Enhancement
#### 4.2.1 Measurement Method
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\begin{tabular}  
As indicated in Table 2, the result of SRCNN exhibits a high SSIM value, while that of the super-resolution generative adversarial network (SRGAN) demonstrated a high BRISQUE value. This result confirmed that an image of quality close to that of visual perception or high-resolution original image was derived. Next, as illustrated in Figure 8, the SR values of five images selected from the bridge image set collected during field inspection are measured. For this image set, unlike other general image sets, no original high-resolution image exists for comparison. Therefore, in this case, the SSIM could not be calculated. Nevertheless, the image quality was assessed based on the measurement of BRISQUE value alone, which is a method of measuring the image quality without comparison. Figure 9 displays the results of image quality enhancement. Table 3 presents the values of BRISQUE measurement. Next, to verify the improvement of the damaged-object-identification performance due to image quality enhancement, the Mask R-CNN model was constructed to perform experiments to detect damaged objects. In this experiment, low- and high-resolution images with image quality enhanced by the SRCNN were used as the input to the damaged-object-identification model. The experiment to identify efflorescent objects was performed among various damaged objects in the image. The parameter values used in the experiment are \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**With Conversion into**} & \multicolumn{2}{c}{**With Conversion into**} \\ \cline{2-5}  & \multicolumn{2}{c}{**High-Resolution Image**} & \multicolumn{2}{c}{**High-Resolution Image**} \\ \cline{2-5}
**Image** & \multicolumn{2}{c}{**BRISQUE**} & \multicolumn{2}{c}{**SSIM**} \\ \cline{2-5}  & **SRCNN** & **Fast-SRGAN** & **SRCNN** & **Fast-SRGAN** \\ \hline
0001x4.png & 45.537 & 30.103 & 0.7779 & 0.7211 \\
0002x4.png & 53.909 & 30.786 & 0.6475 & 0.5849 \\
0003x4.png & 56.625 & 36.472 & 0.6792 & 0.6280 \\
0004x4.png & 57.018 & 32.233 & 0.8819 & 0.8519 \\
0005x4.png & 63.356 & 50.307 & 0.7778 & 0.7326 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: BRISQUE, SSIM measuring result: typical image set. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Image**} & \multicolumn{2}{c}{**BRISQUE**} \\ \cline{2-3}  & **SRCNN** & **Fast-SRGAN** \\ \hline
001\_2018.png & 62.016 & 44.832 \\
002\_2018.png & 58.563 & 29.667 \\
003\_2018.png & 59.600 & 37.275 \\
004\_2018.jpg & 51.470 & 27.746 \\
005\_2018.jpg & 60.091 & 39.898 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: BRISQUE measuring result: real bridge image. Figure 7: Typical set of images used in experiment. The results of efflorescent object identification in the image are summarized in Table 6. Comparing the accuracy of the experimental results between high- and low-resolution images, the specific type of object identification performance was improved in the image converted to high resolution over the low-resolution image. In terms of the true image detection accuracy for the detection of efflorescence, the high-resolution image obtained using the SRCNN performed vastly better than the LR image without requiring image quality enhancement. Compared with the SRGAN, the result of the SRCNN outputting an image similar in quality to the original is appreciable. The SRGAN is more usable as a data-augmentation model that generates similar images when no original is available. The experiment revealed that the model using the image output by the SRCNN had a high detection accuracy. The size of the image was normalized through quality improvement, and pixel data were refined through pixel expansion, which appeared to improve the detection accuracy of the model. Consequently, through the experiment, the results demonstrated that the proposed technology improved the damaged-object-identification performance.