# Deep Lifelong Learning Optimization Algorithm in Dense Region Fusion
## 4 Results and Analysis  
### Experiment Effect of Lifelong Learning Training  
For datasets with small differences in data distribution, a good lifelong learning model can make the model better, as more data becomes available. For optimization algorithms, a good lifetime learning pattern can also provide more effective information for subsequent fusions. To verify the effectiveness of the lifetime learning approach for the defective dataset mentioned in Section 3.2.1, eight object detection algorithms are selected in this section, and trained on the impact hammer sliding dataset according to the experimental setup described in Section 3.3.3. Tables 3-6 relate to \(AP\), \(AP_{50}\), \(AP_{75}\), and \(AR\), respectively. These four metrics demonstrate the performance variation of each algorithm at each stage of lifelong learning. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Algorithm** & **1 th** & **2 th** & **3 th** & **4 th** & **5 th** & **6 th** & **7 th** & **8 th** & **9 th** & **10 th** \\ \hline ATSS & 15.3 & 21.3 & 24.3 & 24 & 25.6 & 27.5 & 26 & 27.2 & 27.1 & 27.7 \\ DDOD & 22.7 & 25.4 & 31 & 30.9 & 31.2 & 30.3 & 31.8 & 31.3 & 31.9 & 33 \\ GFL & 17 & 22.5 & 27.1 & 26.4 & 26.7 & 28.3 & 28.8 & 28.8 & 30.7 & 29.4 \\ Cascade R-CNN & 25.8 & 37.1 & 39.5 & 39.6 & 39.7 & 38.4 & 38.8 & 40 & 39.9 & 39.9 \\ Faster R-CNN & 23.8 & 35.3 & 37.6 & 38.9 & 38.4 & 36.9 & 38.3 & 37.8 & 40.9 & 41.4 \\ Dynamic R-CNN & 27.5 & 32.5 & 35.4 & 38 & 38.8 & 36 & 38.1 & 37.8 & 37.3 & 37.7 \\ Grid R-CNN & 18.2 & 33.8 & 36.6 & 37.2 & 40.3 & 37.6 & 38.6 & 40.5 & 40.6 & 40.2 \\ Libra R-CNN & 23.1 & 30.7 & 32.6 & 35.5 & 34.9 & 34.3 & 35.3 & 36 & 36.2 & 35.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The \(AP\) index of each algorithm at different training stages under the sliding dataset of the shock hammer. The results indicate that the overall performance of the eight object detection algorithms used in the experiment shows an upward trend with the increase in lifelong learning stage across all four metrics. To better illustrate the changes in the model's overall performance, we plotted line charts for each algorithm's performance under the four metrics (\(AP\), \(AP_{50}\), \(AP_{75}\), and \(AR\)) with the training stage, as shown in Figure 6. The results show that all metrics rapidly increased in the first three stages, and the growth rate slowed down in the last seven stages, while the overall trend remained upward. These observations confirm that the lifelong learning training paradigm defined in Section 3.2.1 can effectively demonstrate the performance of various algorithms on the transmission line defect dataset. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**Algorithm** & **1 th** & **2 th** & **3 th** & **4 th** & **5 th** & **6 th** & **7 th** & **8 th** & **9 th** & **10 th** \\ \hline ATSS & 33.3 & 36.7 & 37.9 & 37.2 & 37.4 & 38.5 & 37.4 & 38 & 38.3 & 37.8 \\ DDOD & 36.1 & 36.8 & 39.3 & 38.8 & 39 & 37.3 & 39 & 39.3 & 39.5 & 40.6 \\ GFL & 34.4 & 36.9 & 40 & 41.1 & 41.3 & 40.5 & 41.4 & 40.1 & 42.1 & 40.3 \\ Cascade R-CNN & 35.4 & 44.6 & 46.1 & 45.4 & 45.1 & 43.2 & 43 & 44.6 & 44.5 & 44.6 \\ Faster R-CNN & 35.9 & 43 & 44.1 & 46.2 & 44.7 & 44.2 & 44.7 & 45.1 & 46.1 & 47.3 \\ Dynamic R-CNN & 38.5 & 40.3 & 43 & 46.1 & 46.4 & 43.6 & 45.1 & 46 & 44.6 & 45.7 \\ Grid R-CNN & 30.7 & 41.3 & 43 & 43.2 & 45.7 & 43.8 & 44.6 & 46.8 & 46.8 & 46.9 \\ Libra R-CNN & 38.6 & 42.6 & 44.3 & 45.9 & 46.2 & 45.4 & 46.2 & 46.2 & 46.9 & 46.3 \\ \hline \end{tabular}
\end{table} 
Table 6: The \(AR\) index of each algorithm at different training stages under the sliding dataset of the shock hammer.