# A Deep Learning-Based Fragment Detection Approach for the Arena Fragmentation Test
## 3 Experimental Results
### Performance Evaluation
In Table 5, we compare the performance of (1) (only) Faster R-CNN and (2) Faster R-CNN with temporal filtering in terms of the average precision and recall. From the table, we can see that the Faster R-CNN found the FPFs, and the temporal filtering effectively filtered out the non-FPFs (e.g., the flames). This is due to the high recall in (1) (only) Faster R-CNN and the high precision and recall in (2) Faster R-CNN with temporal filtering. More specifically, the Faster R-CNN ensured s higher recall performance. Then, while keeping the recall performance as much as possible, the temporal filtering raised the precision performance. Figure 14 shows the detection results for the sample image shown in Figure 13. The green box shows the actual first penetrating warhead fragment, the blue box shows the non-first penetrating warhead fragment, and the red box shows the first penetrating warhead fragment detected by the proposed detector. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**The Previous Image Processing-Based Approach [22]**} \\ \hline
**AFT Videos** & **Precision** & **Recall** & \(F_{1}\) & \(F_{0.5}\) \\ \hline Case 1 & \# 1 & 0.800 & 0.571 & 0.666 & 0.741 \\ \hline \multirow{3}{*}{Case 2} & \# 1 & 0.882 & 0.625 & 0.732 & 0.815 \\  & \# 2 & 0.813 & 0.609 & 0.696 & 0.762 \\  & \# 3 & 0.828 & 0.585 & 0.685 & 0.765 \\ \hline \multirow{3}{*}{Case 3} & \# 1 & 0.853 & 0.500 & 0.630 & 0.748 \\  & \# 2 & 1.000 & 0.646 & 0.785 & 0.901 \\  & \# 3 & 0.900 & 0.458 & 0.607 & 0.754 \\ \hline \multirow{3}{*}{Case 4} & \# 1 & 0.618 & 0.955 & 0.750 & 0.665 \\  & \# 2 & 0.692 & 0.947 & 0.800 & 0.731 \\ \cline{1-1}  & \# 3 & 0.500 & 0.714 & 0.588 & 0.532 \\ \cline{1-1}  & \# 4 & 0.414 & 0.857 & 0.558 & 0.462 \\ \hline \multicolumn{6}{c}{Average} & 0.754 & **0.679** & 0.682 & 0.716 \\ \hline \hline \end{tabular} Note that the \(F_{1}\)- and \(F_{0.5}\)-measures indicate the overall performance.  
\end{table} 
Table 4: The quantitative results for the AFT videos in terms of the precision, recall, and F-measures. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Our Proposed Deep Learning-Based Approach**} \\ \hline
**AFT Videos** & **Precision** & **Recall** & \(F_{1}\) & \(F_{0.5}\) \\ \hline Case 1 & \# 1 & 1.000 & 0.429 & 0.600 & 0.790 \\ \hline \multirow{3}{*}{Case 2} & \# 1 & 0.926 & 0.714 & 0.806 & 0.874 \\  & \# 2 & 0.955 & 0.656 & 0.778 & 0.875 \\  & \# 3 & 0.851 & 0.526 & 0.650 & 0.757 \\ \hline \multirow{3}{*}{Case 3} & \# 1 & 0.857 & 0.612 & 0.714 & 0.793 \\  & \# 2 & 0.952 & 0.455 & 0.616 & 0.781 \\  & \# 3 & 1.000 & 0.525 & 0.689 & 0.847 \\ \hline \multirow{3}{*}{Case 4} & \# 1 & 0.857 & 0.857 & 0.857 & 0.857 \\  & \# 2 & 0.833 & 0.833 & 0.833 & 0.833 \\ \cline{1-1}  & \# 3 & 0.714 & 0.833 & 0.769 & 0.735 \\ \cline{1-1}  & \# 4 & 0.688 & 0.917 & 0.786 & 0.724 \\ \hline \multicolumn{5}{c}{Average} & **0.888** & 0.624 & **0.734** & **0.819** \\ \hline \hline \end{tabular} Note that the \(F_{1}\)- and \(F_{0.5}\)-measures indicate the overall performance.  
\end{table} 
Table 3: The quantitative results for the AFT videos in terms of the precision, recall, and F-measures. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{5}{c}{**Avg. Precision**} & **Avg. Recall** \\ \hline Only Faster R-CNN & 0.109 & 0.786 \\ Faster R-CNN + Temporal Filtering & 0.888 & 0.624 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The quantitative comparison for the AFT videos in terms of the precision and recall.