## 3 Results
### Quantitative Results
#### 3.2.2 Object Detection
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Original**} & \multicolumn{2}{c}{**One-Channel**} & \multicolumn{2}{c}{**Three-Channel Grayscale**} & \multicolumn{2}{c}{**Three-Channel Color**} \\ \cline{2-9}  & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** \\ \hline [EMAIL_ADDRESS] & 0.894 & 0.044 & 1.000 & 0.000 & 1.000 & 0.000 & 1.000 & 0.000 \\ \hline [EMAIL_ADDRESS] & 0.857 & 0.072 & 1.000 & 0.000 & 1.000 & 0.000 & 1.000 & 0.000 \\ \hline [EMAIL_ADDRESS] & 0.817 & 0.079 & 1.000 & 0.000 & 1.000 & 0.000 & 1.000 & 0.000 \\ \hline [EMAIL_ADDRESS] & 0.727 & 0.092 & 1.000 & 0.000 & 1.000 & 0.000 & 1.000 & 0.000 \\ \hline [EMAIL_ADDRESS] & 0.588 & 0.101 & 1.000 & 0.000 & 1.000 & 0.000 & 1.000 & 0.000 \\ \hline [EMAIL_ADDRESS] & 0.397 & 0.049 & 0.999 & 0.002 & 0.999 & 0.001 & 1.000 & 0.000 \\ \hline [EMAIL_ADDRESS] & 0.224 & 0.061 & 0.986 & 0.016 & 0.996 & 0.001 & 0.997 & 0.000 \\ \hline [EMAIL_ADDRESS] & 0.077 & 0.033 & 0.972 & 0.017 & 0.989 & 0.001 & 0.988 & 0.002 \\ \hline [EMAIL_ADDRESS] & 0.016 & 0.015 & 0.767 & 0.022 & 0.885 & 0.034 & 0.860 & 0.045 \\ \hline [EMAIL_ADDRESS] & 0.001 & 0.001 & 0.192 & 0.086 & 0.234 & 0.052 & 0.275 & 0.064 \\ \hline [EMAIL_ADDRESS]:0.95 & 0.460 & 0.040 & 0.892 & 0.009 & 0.910 & 0.007 & 0.912 & 0.008 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: YOLOv4 object detection validation results with the original dataset. Figure 14: Boxplots of validation [EMAIL_ADDRESS]:0.95 with YOLOv4: (**a**) results validate with the augmented dataset; (**b**) results validate with the original dataset. We also tabulated average and standard deviations for all mAPs of trained YOLOv4-tiny networks. Each mAP and [EMAIL_ADDRESS]:0.95 by augmented data are shown in Table 12, and corresponding results by the original data are shown in Table 13.  
Similar to the results of YOLOv4, Tables 12 and 13 show the tendency in which the IoU threshold increases and the mAP value decreases. In Table 12, [EMAIL_ADDRESS] of YOLOv4-tiny networks trained with the three-channel augmented color dataset is lower than that of networks trained with the three-channel augmented grayscale dataset. In Table 13, [EMAIL_ADDRESS] and [EMAIL_ADDRESS] of YOLOv4-tiny networks trained with the three-channel augmented color dataset are lower than that of networks trained with the three-channel augmented grayscale dataset. However, in most cases, YOLOv4-tiny networks trained with the three-channel augmented color dataset have the highest mAP value. The boxplot of mAP0.5:0.95 obtained from both datasets is shown in Figure 15. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Original**} & \multicolumn{2}{c}{**One-Channel**} & \multicolumn{2}{c}{**Three-Channel Grayscale**} & \multicolumn{2}{c}{**Three-Channel Color**} \\ \cline{2-10}  & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** \\ \hline [EMAIL_ADDRESS] & 0.857 & 0.044 & 1.000 & 0.000 & 1.000 & 0.000 & 1.000 & 0.000 \\ \hline [EMAIL_ADDRESS] & 0.761 & 0.067 & 1.000 & 0.000 & 1.000 & 0.000 & 1.000 & 0.000 \\ \hline [EMAIL_ADDRESS] & 0.761 & 0.067 & 1.000 & 0.000 & 1.000 & 0.000 & 1.000 & 0.000 \\ \hline [EMAIL_ADDRESS] & 0.620 & 0.080 & 1.000 & 0.000 & 1.000 & 0.000 & 1.000 & 0.000 \\ \hline [EMAIL_ADDRESS] & 0.464 & 0.075 & 1.000 & 0.000 & 1.000 & 0.000 & 1.000 & 0.000 \\ \hline [EMAIL_ADDRESS] & 0.306 & 0.061 & 1.000 & 0.000 & 1.000 & 0.000 & 1.000 & 0.000 \\ \hline [EMAIL_ADDRESS] & 0.160 & 0.045 & 0.996 & 0.003 & 0.999 & 0.003 & 0.998 & 0.002 \\ \hline [EMAIL_ADDRESS] & 0.070 & 0.019 & 0.983 & 0.005 & 0.989 & 0.006 & 0.986 & 0.005 \\ \hline [EMAIL_ADDRESS] & 0.013 & 0.009 & 0.923 & 0.029 & 0.939 & 0.017 & 0.951 & 0.015 \\ \hline [EMAIL_ADDRESS] & 0.001 & 0.001 & 0.593 & 0.049 & 0.645 & 0.079 & 0.704 & 0.050 \\ \hline [EMAIL_ADDRESS] & 0.000 & 0.000 & 0.077 & 0.052 & 0.068 & 0.049 & 0.097 & 0.036 \\ \hline [EMAIL_ADDRESS]:0.95 & 0.325 & 0.028 & 0.857 & 0.011 & 0.864 & 0.012 & 0.874 & 0.007 \\ \hline \hline \end{tabular}
\end{table}