# Deep Lifelong Learning Optimization Algorithm in Dense Region Fusion
## 4 Results and Analysis
### Horizontal Comparison Experiment with Other Algorithms
#### 4.3.4 Comparison between DLLO-DRF and Other Algorithms in the Insulator Self-Explosion Dataset  
As shown in Table 14, DLLO-DRF ranks first for three or more metrics among six detection algorithms. For the \(AP\) metric, DLLO-DRF ranks first among seven detection algorithms. For the \(AP_{50}\) metric, DLLO-DRF ranks first among seven detection algorithms. For the \(AP_{75}\) metric, DLLO-DRF ranks first among three detection algorithms. For the \(AR\) metric, DLLO-DRF ranks first among seven detection algorithms. In contrast, for Uniform setup, there are one, two, three, and two algorithms ranking first for the \(AP\), \(AP_{50}\), \(AP_{75}\), and \(AR\) metrics, respectively, and for Greedy soup, there are one, one, and four algorithms ranking first for the \(AP\), \(AP_{50}\), and \(AP_{75}\) metrics, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Object Algorithm** & **Fusion Method** & \(AP\) & \(AP_{50}\) & \(AP_{75}\) & \(AR\) \\ \hline \multirow{3}{*}{ATSS} & Uniform soup & 13.4 & 37.5 & 9.2 & **28.3** \\  & Greedy soup & 13.3 & 37.2 & 9.2 & 28.2 \\  & DLLO-DRF & **13.4** & **37.6** & **9.3** & 28.2 \\ \hline \multirow{3}{*}{DDOD} & Uniform soup & 15.2 & 45.9 & **12.3** & 20.4 \\  & Greedy soup & 15.3 & 46.5 & **12.3** & 20.8 \\  & DLLO-DRF & **15.6** & **47.2** & 12.2 & **21.2** \\ \hline \multirow{3}{*}{GFL} & Uniform soup & 21.4 & 42.2 & 21.7 & 29.2 \\  & Greedy soup & 21.4 & 42.5 & **22.1** & 28.9 \\  & DLLO-DRF & **21.9** & **44.8** & 18.7 & **30.7** \\ \hline \multirow{3}{*}{Cascade R-CNN} & Uniform soup & **17.5** & 34.9 & **16.5** & **21.8** \\  & Greedy soup & 17.2 & 34.7 & 16 & 21.7 \\  & DLLO-DRF & 16.9 & **35.4** & 15.2 & **21.8** \\ \hline \multirow{3}{*}{Faster R-CNN} & Uniform soup & 15.8 & 30.4 & 15 & 21.6 \\  & Greedy soup & 16.2 & 31.9 & 14.8 & 22.3 \\  & DLLO-DRF & **17** & **33.5** & **16.5** & **22.7** \\ \hline \multirow{3}{*}{Dynamic R-CNN} & Uniform soup & 12.8 & 25.4 & 9.9 & 19.3 \\  & Greedy soup & 12.4 & 25.1 & 9.4 & 18.4 \\  & DLLO-DRF & **13.4** & **26** & **10.5** & **20.1** \\ \hline \multirow{3}{*}{Grid R-CNN} & Uniform soup & 15.5 & 36 & **10.5** & 22.2 \\  & Greedy soup & 15.7 & 36 & 10.4 & 22.4 \\  & DLLO-DRF & **16** & **37.8** & 9.1 & **22.6** \\ \hline \multirow{3}{*}{Libra R-CNN} & Uniform soup & **18.7** & 45.2 & **14** & 23.7 \\  & Greedy soup & 18.1 & **45.4** & 13.5 & 23.6 \\  & DLLO-DRF & 18.2 & 45.3 & 13.3 & **23.9** \\ \hline \multirow{3}{*}{Sparse R-CNN} & Uniform soup & 17.5 & 46.7 & **5.5** & 25.3 \\ \cline{1-1}  & Greedy soup & 17.2 & 45.8 & 3.7 & 24.8 \\ \cline{1-1}  & DLLO-DRF & **18.3** & **47.7** & 5 & **26** \\ \hline \end{tabular}
\end{table}
Table 12: Comparison of DLLO-DRF and other fusion methods in the cement pole damage dataset. The better results are indicated using bold and underline.  
In conclusion, DLLO-DRF outperforms Uniform soup and Greedy soup on all four datasets, and Greedy soup performs better than Uniform soup. The model faces significant parameter update deviations when encountering new data at each training stage under the training conditions based on fine-tuning lifelong learning. DLLO-DRF strikes a balance between fundamental parameters to identify and compute parameters that are appropriate for the entire lifelong learning process, while Uniform soup and Greedy soup integrate all basic parameters, resulting in a weaker expression ability of the integrated model for all data in the entire lifelong learning process. As Greedy soup aims to identify the best metrics, and uses a greedy approach to identify suitable subsets of basic models for fusion, it is more suitable for lifelong learning as a dynamic training method compared to Uniform soup.