# Deep Lifelong Learning Optimization Algorithm in Dense Region Fusion
## 4 Results and Analysis
### Longitudinal Comparative Experiments on Basic Algorithms
#### 4.2.2 Comparison of DLLO-DRF Detection Algorithms Used and Unused in the Cement Pole Damage Dataset  
Table 8 shows that using DLLO-DRF improved the average values of the metrics for each algorithm in the cement pole damage dataset, with improvements observed in \(AP\), \(AP_{50}\), and \(AR\). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{4}{c}{**Using DILO-DRF**} & \multicolumn{4}{c}{**Without DLLO-DRF**} \\ \cline{2-9}  & _AP_ & \(AP_{50}\) & \(AP_{75}\) & \(AR\) & \(AP\) & \(AP_{50}\) & \(AP_{75}\) & \(AR\) \\ \hline ATSS & 26.5 & **66** & **12.5** & **39.8** & **26.8** & 65.7 & 11.6 & 37.7 \\ DDOD & 30.1 & **67.2** & **16.6** & 42.4 & **30.5** & 66.8 & 16.2 & **42.8** \\ GFL & 19.3 & **46.9** & **11.6** & **36.6** & **19.5** & 46.4 & 10.6 & 33.2 \\ Cascade R-CNN & **30.2** & **70.1** & **14.4** & 37.6 & 29.7 & 70 & 12.9 & **37.7** \\ Faster R-CNN & **31.1** & **73** & 14.2 & **38.1** & 30.4 & 72.9 & 14.2 & 38 \\ Dynamic R-CNN & 27.7 & **68.8** & **11.5** & 36.5 & **27.9** & 67.1 & 10.8 & **37.2** \\ Grid R-CNN & **29.8** & **65.2** & 23.3 & **38.4** & 29.5 & 64.7 & **23.6** & 38 \\ Libra R-CNN & 26.6 & 62.9 & **13.2** & **37.2** & **26.8** & **64.2** & 12.3 & 36.9 \\ Sparse R-CNN & 25.4 & 65.2 & **11.5** & **40.9** & **25.5** & **65.5** & 11 & 40.8 \\ Average & 27.4 & **65** & **14.3** & **38.6** & 27.4 & 64.8 & 13.7 & 38 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of DLLO-DRF algorithms used and unused in the bird’s nest foreign object dataset. The better results are indicated using bold and underline. Figure 6: Trends in metrics of various algorithms in different stages of lifelong learning training. 2.3 Comparison of DLLO-DRF Detection Algorithms Used and Unused in the Inselator dataset. The better results are indicated using bold and underline. Table 9 demonstrates that using DLLO-DRF improved the average values of the metrics for each algorithm in the shockproof hammer slip dataset, with improvements observed in \(AP\), \(AP_{50}\), and \(AR\). 2.4 Comparison of DLLO-DRF Detection Algorithms Used and Unused in the Inselator dataset. The better results are indicated using bold and underline. Table 10 illustrates that using DLLO-DRF improved the average values of the metrics for each algorithm in the insulator self-explosion dataset, with improvements observed in \(AP\), \(AP_{50}\), \(AP_{75}\), and \(AR\). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{4}{c}{**Using DILO-DRF**} & \multicolumn{4}{c}{**Without DLLO-DRF**} \\ \cline{2-10}  & \(AP\) & \(AP_{50}\) & \(AP_{75}\) & \(AR\) & \(AP\) & \(AP_{50}\) & \(AP_{75}\) & \(AR\) \\ \hline ATSS & **13.1** & **41.8** & 3.6 & **34.8** & 13 & 41.6 & 3.6 & 34.5 \\ DDOD & **19.6** & 53.9 & **8.3** & 39.5 & 19.4 & 53 & 6.7 & 38.8 \\ GFL & **16.3** & **46** & 5.5 & **37.8** & 16.2 & 45.6 & 5.5 & 37.7 \\ Cascade R-CNN & **29.5** & **67.3** & **19.9** & 43.7 & 28.9 & 65.9 & 19.6 & 43.7 \\ Faster R-CNN & **16.1** & **45.3** & 7.9 & 31.6 & 16 & 44.9 & 7.9 & **31.7** \\ Dynamic R-CNN & **23.6** & **61.2** & 13.4 & 37.2 & 23.2 & 60.6 & **13.6** & **37.4** \\ Grid R-CNN & **27.6** & **60.9** & 19.7 & 46 & 27.5 & 60.4 & **20.1** & **46.2** \\ Libra R-CNN & **21.1** & **52.6** & **14.2** & 37.2 & 20.3 & 51.1 & 13.7 & 37 \\ Sparse R-CNN & **20.8** & 49.4 & **15.3** & **37.5** & 20.7 & 49.4 & 15.2 & 37.3 \\ Average & **20.9** & **53.2** & **12** & **38.4** & 20.6 & 52.5 & 11.8 & 38.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of DLLO-DRF algorithms used and unused in the insulator self-explosion dataset. The better results are indicated using bold and underline.