## 4 Results
### Evaluation and Analysis of Follow-Up Effects
#### 4.3.2 Experimental Results  
Firstly, the proposed SiamRPN-based tracking and localization method for punching the robotic arm in this study is compared with the original benchmark algorithm, SiamRPN, and the obtained success and accuracy rates are shown in Figures 7(a) and 7(b), respectively. Both in terms of the success rate and the accuracy rate, the method of this study has a better result compared to the benchmark algorithm, with an increase of the success rate by 1.6% and the accuracy rate by 1.7%. Not only the improved target tracking method of this study is compared with the original SiamRPN algorithm, but also several classical target tracking algorithms related to the method of this study are selected for comparison, which includes the breakthrough algorithms SiamFC using a twin network, Staple [22] using deep convolutional features and DSST [23] based on correlation filtering, SRDCF [24], and CFNet [25]. As shown in Figure 9, this research algorithm still outperforms other classical target tracking algorithms; Table 4 details the comparison data of the proposed method with the benchmark algorithm and other classical algorithms in terms of success rate and accuracy, where the success rate of this research method reaches 0.639 and the accuracy rate is 0.861. Figure 8: Comparison of success rate and accuracy rate between proposed algorithm and baseline algorithm. The execution of the tracking task of the perforating robotic arm is carried out in a dynamic environment, which is characterized by a high degree of uncertainty. The perforating robotic arm itself is in a constant state of flux, and a variety of unanticipated challenges may arise at any time. A series of experimental trials were conducted to comprehensively examine the adaptability and efficacy of the SiamRPN-based perforating robotic arm tracking and localization methodology employed in this investigation within a variable and intricate environment. The tests addressed a series of scenarios: blur, fast motion, low resolution, occlusion, in-plane and out-of-plane rotation, out-of-field, out-of-view, and background clutter. The results of these tests were then compared, and the resulting accuracy and success curves are presented in Figures 10 and 11 for visual comparison and analysis of performance. The experimental results demonstrate that the method proposed in this study demonstrates superior performance to classical algorithms when confronted with a variety of complex scenarios, including motion blur, fast movement, background clutter, occlusion, and out-of-plane rotation. In particular, there is a significant improvement of 4.0% in accuracy and a 4.4% increase in success rate in scenes with occlusion. In scenes outside the field of view, there was an 8.7% increase in accuracy and a 7.6% increase in success rate. These results validate the efficacy of the [PERSON] filter-based target prediction algorithm and the target detection-based repositioning method for the perforating robotic arm used in this study, demonstrating an improvement in system determination efficiency and accuracy when dealing with occlusion and out-of-field-of-view scenes. While the success rate is not the highest in low-resolution and in-plane rotation scenes, it is not far from the optimal method. \begin{table}
\begin{tabular}{c c c} \hline
**Algorithm** & **Success** & **Precision** \\ \hline Ours & 0.639 & 0.861 \\ SiamRPN & 0.629 & 0.847 \\ SiamFC & 0.586 & 0.772 \\ Staple & 0.578 & 0.783 \\ DSST & 0.517 & 0.686 \\ SRDCF & 0.598 & 0.789 \\ CFNet & 0.587 & 0.778 \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison of tracking performance between proposed algorithm and classical algorithms. Figure 9: Comparison of success rate and accuracy rate between proposed algorithm and classical algorithm. Figure 10: Comparison of accuracy rates between the proposed method and the classical algorithm. Figures are arranged in three columns per row.