## 5 Discussion
### Easy-Sample Threshold Analysis  
The smaller the threshold, the more samples may be filtered as easy samples by the cascade RPNs. Taking B \(\rightarrow\) A as an example, we implemented the experiment with threshold values of 0.95, 0.96, 0.97, 0.98, 0.99, 0.999, and 0.9999. The results are shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**RPN1**} & \multirow{2}{*}{**RPN3**} & \multirow{2}{*}{**RPN3**} & \multicolumn{6}{c}{**A \(\rightarrow\) C**} & \multicolumn{6}{c}{**C \(\rightarrow\) A**} \\ \cline{5-10}  & & & & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline SW & & & ✓ & 63.55 & 61.29 & 62.40 & 26.19 & 30.80 & 28.31 \\  & ✓ & & ✓ & **84.21** & 55.43 & 66.85 & 57.93 & 55.67 & 56.78 \\ Ours & & ✓ & ✓ & 66.30 & 61.07 & 63.58 & 32.04 & 31.31 & 31.67 \\  & ✓ & ✓ & ✓ & 73.47 & **63.18** & **67.94** & **84.13** & **88.96** & **86.48** \\ \hline \hline \end{tabular}
\end{table}
Table 19: Effects of cascade stages on A \(\rightarrow\) B and B \(\rightarrow\) A. The best F1-score was reached when three RPNs were added.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**RPN1**} & \multirow{2}{*}{**RPN3**} & \multirow{2}{*}{**RPN3**} & \multicolumn{6}{c}{**A \(\rightarrow\) B**} & \multicolumn{6}{c}{**B \(\rightarrow\) A**} \\ \cline{5-10}  & & & & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline SW & & & ✓ & 72.94 & **87.75** & 79.66 & **96.54** & 88.52 & 92.54 \\  & ✓ & & ✓ & 75.84 & 87.61 & 81.30 & 96.36 & 92.19 & 94.23 \\ Ours & & ✓ & ✓ & 74.41 & 87.39 & 80.38 & 94.94 & 91.54 & 93.21 \\  & ✓ & ✓ & ✓ & **78.11** & 87.14 & **82.38** & 96.00 & **93.08** & **94.52** \\ \hline \hline \end{tabular}
\end{table}
Table 17: Effects of cascade stages on B \(\rightarrow\) C and C \(\rightarrow\) B. The best F1-score was reached when three RPNs were added.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**RPN1**} & \multirow{2}{*}{**RPN3**} & \multirow{2}{*}{**RPN3**} & \multicolumn{6}{c}{**A \(\rightarrow\) B**} & \multicolumn{6}{c}{**B \(\rightarrow\) A**} \\ \cline{5-10}  & & & & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline SW & & & ✓ & 72.94 & **87.75** & 79.66 & **96.54** & 88.52 & 92.54 \\  & ✓ & & ✓ & 75.84 & 87.61 & 81.30 & 96.36 & 92.19 & 94.23 \\ Ours & & ✓ & ✓ & 74.41 & 87.39 & 80.38 & 94.94 & 91.54 & 93.21 \\  & ✓ & ✓ & ✓ & **78.11** & 87.14 & **82.38** & 96.00 & **93.08** & **94.52** \\ \hline \hline \end{tabular}
\end{table}
Table 19: Effects of cascade stages on A \(\rightarrow\) B and B \(\rightarrow\) A. The best F1-score was reached when three RPNs were added.  
When the threshold was less than or equal to 0.99, the cascade RPNs filtered out easy samples and improved the performance of the classifier, and the F1-score increased with the increase of the threshold. When the threshold was greater than 0.99, only a few samples were filtered, and the F1-score decreased significantly. Therefore, we finally selected 0.99 as the easy-sample threshold to obtain the highest F1-score.