## 5 Ablation Study
### Effect of Input Image Resolution  
Table 4 shows the experiment results for each model according to the input image resolution. The detection times for each resolution had a maximum of 4 s with extremely large differences, which is due to changes in the number of candidate regions based on the resolution. As the resolution increased, the noise in the background region also increased, and the candidate regions increased by a factor of greater than 2, which increased the processing time and misdetection rate. By contrast, when the resolution was too low, some of the pit defect regions were lost, and the number of non-detections increased. In the case of MobileNet, when n was 4, there was only a 1% difference in precision compared  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Resolution** & **Precision (\%)** & **Recall (\%)** & **Acc (\%)** & **Time (s)** \\ \hline RT-SPEeDet-R & \multirow{2}{*}{1638 \(\times\) 1200} & 70.4 & 73.5 & 98.1 & 2.6 \\ RT-SPEeDet-M & & (n = 5) & 97.6 & 60.3 & 99.0 & 0.55 \\ RT-SPEeDet-S & & 98.0 & 73.5 & 99.7 & 1.73 \\ \hline RT-SPEeDet-R & \multirow{2}{*}{2048 \(\times\) 1500} & 71.1 & 79.4 & 99.2 & 2.28 \\ RT-SPEeDet-M & & 97.2 & 51.5 & 99.2 & 0.47 \\ RT-SPEeDet-S & & 98.2 & 79.4 & 99.7 & 1.48 \\ \hline RT-SPEeDet-R & \multirow{2}{*}{2730 \(\times\) 2000} & 47.1 & 83.8 & 99.3 & 6.22 \\ RT-SPEeDet-M & & 80.0 & 35.3 & 99.5 & 1.07 \\ RT-SPEeDet-S & & 96.5 & 80.9 & 99.3 & 3.99 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Small pit defect detection test results according to input image resolution.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Augmentation**} & \multirow{2}{*}{**Baseline**} & \multicolumn{2}{c}{**Auto Contrast**} & \multicolumn{2}{c}{**Adjust Bright**} & \multicolumn{2}{c}{**Random Rotation**} & \multirow{2}{*}{**Time**} \\ \cline{2-3} \cline{5-10}  & & **Precision** & **Recall** & **Precision** & **Recall** & **Precision** & **Recall** & **Precision** & **Recall** \\ \multirow{2}{*}{**Model**} & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline RT-SPEeDet-R & \multirow{2}{*}{71.1} & \multirow{2}{*}{79.4} & 61.5 & 82.4 & 29.5 & 82.4 & 45.5 & 82.4 & \multirow{2}{*}{2.28} \\  & & & (\(-\)9.6) & (\(+\)3) & (\(-\)41.6) & (\(+\)3) & (\(-\)25.6) & (\(+\)3) & \\ \hline RT-SPEeDet-M & \multirow{2}{*}{97.2} & \multirow{2}{*}{51.5} & 88.7 & 80.9 & 49.6 & 82.4 & 53.5 & 55.9 \\  & & & (\(-\)8.5) & (\(+\)29.4) & (\(-\)47.6) & (\(+\)30.9) & (\(-\)43.7) & (\(+\)4) & \\ \hline RT-SPEeDet-S & \multirow{2}{*}{98.2} & \multirow{2}{*}{79.4} & 84.6 & 80.9 & 31.2 & 80.9 & 98.2 & 82.4 \\  & & & (\(-\)13.6) & (\(+\)1.5) & (\(-\)67) & (\(+\)1.5) & (0) & (\(+\)3) & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Pit defect detection results according to the data augmentation techniques applied.  
to SqueezeNet, whereas it achieved more than 3 times faster speed at 0.47 s. Since the proposed two-stage detector RT-SPeeDet is compatible structure, converting classifier to lightweight architecture makes it possible to achieve processing speeds of less than 1 s. This shows the proposed model is efficient in terms of structure.