# Deep Lifelong Learning Optimization Algorithm in Dense Region Fusion
## 5 Conclusions  
This paper presents DLLO-DRF, a deep lifelong learning optimization algorithm based on dense region fusion. In this algorithm, the parameters at each stage in lifelong learning are partitioned into regions based on their values. DLLO-DRF identifies dense regions through parameter dispersion analysis, and optimizes the model by averaging and fusing the parameters. In Section 4, extensive experiments on the self-labeled transmission line defect dataset and various deep object detection algorithms demonstrate that the proposed DLLO-DRF effectively improves the performance of deep neural network models under lifelong learning. This significantly contributes to the development of deep lifelong learning by offering an optimization method that simultaneously allows for the learning of new data without using old data, and without increasing the model parameters. Additionally, as deep lifelong learning is a rapidly growing and evolving field, there are many areas in which DLLO-DRF can be further improved and developed. Further research is required to evaluate the effectiveness of DLLO-DRF on diverse and large-scale datasets. It is also important to explore ways of adapting DLLO-DRF to optimize complex, changing deep neural network models, and make them scalable for large-scale lifelong learning scenarios. Overall, the proposed DLLO-DRF offers a promising approach for optimizing lifelong learning in deep neural networks, and opens up new avenues for future research and development. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Object Algorithm** & **Fusion Method** & _AP_ & _AP_\({}_{\text{50}}\) & _AP_\({}_{\text{75}}\) & _AR_ \\ \hline \multirow{3}{*}{ATSS} & Uniform soup & 12.5 & 41.7 & 3.2 & 34.5 \\  & Greedy soup & 12.6 & 41.5 & 3.2 & 34.6 \\  & DLLO-DRF & **13.1** & **41.8** & **3.6** & **34.8** \\ \hline \multirow{3}{*}{DDOD} & Uniform soup & **19.8** & **55** & 8 & 39.1 \\  & Greedy soup & **19.8** & **55** & **8.5** & 38.8 \\  & DLLO-DRF & 19.6 & 53.9 & 8.3 & **39.5** \\ \hline \multirow{3}{*}{GFL} & Uniform soup & 16.6 & **48.6** & **6.8** & **38.5** \\  & Greedy soup & **16.7** & 48.4 & **6.8** & 38.2 \\  & DLLO-DRF & 16.3 & 46 & 5.5 & 37.8 \\ \hline \multirow{3}{*}{Cascade R-CNN} & Uniform soup & 29.1 & 66.1 & 21.9 & 42.5 \\  & Greedy soup & 28.8 & 65.3 & **22.5** & 42.5 \\  & DLLO-DRF & **29.5** & **67.3** & 19.9 & **43.7** \\ \hline \multirow{3}{*}{Faster R-CNN} & Uniform soup & 14.5 & 41.3 & 8.6 & 30.4 \\  & Greedy soup & 15 & 42.9 & **9** & 31.3 \\  & DLLO-DRF & **16.1** & **45.3** & 7.9 & **31.6** \\ \hline \multirow{3}{*}{Dynamic R-CNN} & Uniform soup & 22.4 & 59.9 & 13.3 & 36.4 \\  & Greedy soup & 21.6 & 59.9 & 9.6 & 35.3 \\  & DLLO-DRF & **23.6** & **61.2** & **13.4** & **37.2** \\ \hline \multirow{3}{*}{Grid R-CNN} & Uniform soup & 26.8 & 59.1 & **20.9** & **46.7** \\  & Greedy soup & 27 & 60.1 & 19.1 & 46.2 \\  & DLLO-DRF & **27.6** & **60.9** & 19.7 & 46 \\ \hline \multirow{3}{*}{Libra R-CNN} & Uniform soup & 20.2 & 50 & 13.4 & 35.5 \\  & Greedy soup & 20.9 & 49.8 & **15.1** & 35.8 \\  & DLLO-DRF & **21.1** & **52.6** & 14.2 & **37.2** \\ \hline \multirow{3}{*}{Sparse R-CNN} & Uniform soup & 20 & 48.9 & 12.2 & 36.7 \\  & Greedy soup & 20.1 & 48.2 & 13.3 & 36.2 \\ \cline{1-1}  & DLLO-DRF & **20.8** & **49.4** & **15.3** & **37.5** \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Comparison of DLLO-DRF and other fusion methods in the insulator self-explosion dataset. The better results are indicated using bold and underline. **Author Contributions:** Conceptualization, L.Z., F.D., S.X. and H.W. ; methodology, L.Z., F.D. and S.X. ; software, F.D. ; validation, Z.C., H.W. and F.D. ; formal analysis, L.Z. ; investigation, L.Z. ; resources, F.D., S.X., Z.C. and H.W. ; data curation, L.Z. ; writing--original draft preparation, L.Z., F.D., S.X., Z.Q. and Z.C. ; writing--review and editing, L.Z., F.D., S.X., Z.Q., Z.C. and H.W. ; visualization, L.Z. and F.D. ; supervision, Z.C. and H.W. ; project administration, H.W.