# Deep Lifelong Learning Optimization Algorithm in Dense Region Fusion
## 4 Results and Analysis
### Friedman Test Results  
To investigate the differences between the three abovementioned algorithms, the Friedman test method was used to verify the experiment in Section 4.3. Here, the \(AP\) metric is selected for testing all datasets and basic object detection algorithms. The experiment involved three algorithms and used four datasets, and \(\tau_{F}\) followed the \(F\) distribution with degrees of freedom of 2 and 6. According to the \(F(2,6)\) distribution, the \(\rho\) values for ATSS, DDOD, GFL, Cascade R-CNN, Fast R-CNN, Dynamic R-CNN, Grid R-CNN, Libra R-CNN, and Sparse R-CNN are 0.0293, 0.0293, 0.0293, 0.0421, 0.0293, 0.0293, 0.0293, 0.0293, and 0.0388, respectively, with significance less than 0.05. As a result, we must reject the initial assumption that there is no significant difference in the performance of each algorithm. Therefore, there are significant differences in the performance of the algorithms participating in the experiment. Based on the conclusion of the experiment in  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Object Algorithm** & **Fusion Method** & \(AP\) & \(AP_{\mathbf{30}}\) & \(AP_{\mathbf{75}}\) & \(AR\) \\ \hline \multirow{3}{*}{ATSS} & Uniform soup & 27.9 & 63.8 & 18.3 & 37.6 \\  & Greedy soup & 27.8 & 63.9 & 17.4 & 37.8 \\  & DLLO-DRF & **28.2** & **64** & **19.4** & **38** \\ \hline \multirow{3}{*}{DDOD} & Uniform soup & 31.8 & 64.1 & 29.7 & 39.6 \\  & Greedy soup & 32.3 & 65.8 & 30.1 & 40.1 \\  & DLLO-DRF & **33.2** & **66.8** & **31.7** & **40.8** \\ \hline \multirow{3}{*}{GFL} & Uniform soup & 29 & 63.9 & 19.4 & 40.6 \\  & Greedy soup & 29.1 & 63.8 & 19.2 & 40.9 \\  & DLLO-DRF & **29.8** & **64.2** & **20.1** & **41.1** \\ \hline \multirow{3}{*}{Cascade R-CNN} & Uniform soup & 39.4 & 61.1 & 48.1 & 44.3 \\  & Greedy soup & **39.9** & 61.8 & **48.6** & **44.8** \\  & DLLO-DRF & 39.6 & **62** & 48.3 & 44.4 \\ \hline \multirow{3}{*}{Faster R-CNN} & Uniform soup & 40.5 & 66.5 & 46.1 & 45.8 \\  & Greedy soup & 40.5 & 66.4 & **46.8** & 45.7 \\  & DLLO-DRF & **40.9** & **67.3** & 46.5 & **46.7** \\ \hline \multirow{3}{*}{Dynamic R-CNN} & Uniform soup & 38.5 & 60.2 & 43.6 & 46 \\  & Greedy soup & **38.8** & 60.4 & **43.7** & **46.1** \\  & DLLO-DRF & 38 & **60.6** & 42.6 & 46 \\ \hline \multirow{3}{*}{Grid R-CNN} & Uniform soup & 40.3 & **64.4** & 44.7 & 46.9 \\  & Greedy soup & **40.7** & **64.4** & **45.6** & **47.4** \\  & DLLO-DRF & 40.2 & **64.4** & 43.6 & 47 \\ \hline \multirow{3}{*}{Libra R-CNN} & Uniform soup & 35.3 & 60 & 37.7 & 46.2 \\  & Greedy soup & 35.3 & 60.4 & 37.7 & 46.2 \\  & DLLO-DRF & **35.9** & **61.1** & **38.2** & **46.5** \\ \hline \multirow{3}{*}{Sparse R-CNN} & Uniform soup & 17.4 & 38.7 & 13.8 & 42 \\  & Greedy soup & 17.6 & 38.9 & 14 & 42.1 \\ \cline{1-1}  & DLLO-DRF & **18.3** & **40.3** & **14.6** & **43.5** \\ \hline \hline \end{tabular}
\end{table}
Table 13: Comparison of DLLO-DRF and other fusion methods in the shockproof hammer slip dataset. The better results are indicated using bold and underline.  
Section 4.3, it is concluded that DLLO-DRF has the best performance compared to the comparative algorithms.