## 4 Results
### Evaluation and Analysis of First-Frame Localization Effects  
The primary objective of the initial frame localization model in this study is to accurately and expeditiously localize the punching robot arm based on RFBNet. Consequently, the original RFBNet model is subjected to a comparative and analytical evaluation with the enhanced RFBNet in this study. The findings are presented in Table 2.  
The tabular data demonstrate that the enhancements made to the RFBNet model in this study markedly enhance the average accuracy and frames per second (FPS) while simultaneously reducing the model size. The average accuracy of the enhanced model is 0.94, representing a 3.3% improvement over the original RFBNet model. Additionally, the FPS has reached 31. These outcomes demonstrate that the detection precision and speed of the punching robotic arm can be significantly enhanced through the integration of ShuffleNet V2 and GIoU into the conventional RFBNet network architecture and the optimization of the loss function. To observe the comparative effect of the initial frame localization model based on the enhanced RFBNet before and after the incorporation of each improvement strategy, the model was subjected to an ablation experiment, the results of which are presented in Table 3 below. In this study, when ShuffleNet V2 was adopted as the backbone network, the initial average precision (AP) dropped to 0.89, primarily due to the reduced channel capacity in its architecture. ShuffleNet V2 is designed as a lightweight model, utilizing channel splitting and depthwise separable convolutions to reduce computational complexity and parameter count. However, while this design improves computational efficiency and reduces model size, it also limits the network's capacity to represent complex details and multi-scale features, leading to an initial decline in AP. To compensate for this loss, the Receptive Field Block (RFB) module was introduced, incorporating dilated convolutions. Dilated convolutions expand the receptive field, allowing the network to capture a broader range of contextual information, particularly for multi-scale feature extraction. Through these extended convolution operations, the RFB module enhances the network's ability to perceive objects across different scales, effectively restoring multi-scale feature extraction capability. Thus, although ShuffleNet V2 reduces computational complexity, the integration of the RFB module mitigates the loss of multi-scale information and improves detection accuracy. Furthermore, to optimize bounding box regression, the study introduces the Generalized Intersection over Union (GIoU) loss function. Unlike traditional IoU loss, GIoU incorporates the concept of an enclosing box to provide a more comprehensive evaluation of the similarity between the predicted and ground truth boxes. GIoU considers not only the overlapping region but also the non-overlapping areas, leading to more precise \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Algorithm** & **Average Precision (AP)** & **FPS** & **Model Size (MB)** \\ \hline Improved RFBNet & 0.94 & 31 & 135.5 \\ RFBNet & 0.91 & 24 & 146.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Model parameter comparison. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model Name** & **Average Precision (AP)** & **FPS** & **Model Size (MB)** \\ \hline RFBNet & 0.91 & 24 & 146.3 \\ RFBNet + ShuffleNet V2 & 0.89 & 30 & 131.3 \\ RFBNet + ShuffleNet V2 + GIoU & 0.92 & 31 & 132.1 \\ RFBNet + ShuffleNet V2 + GIoU + loss & 0.94 & 31 & 135.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison results of model ablation experiments. gradients and addressing the gradient vanishing issue encountered in IoU when bounding box overlap is low. With the introduction of GIoU loss, the model's AP further increased to 0.92, demonstrating its crucial role in refining bounding box regression and enhancing localization accuracy. Finally, by integrating Focal Loss, the model's precision was further improved, ultimately reaching an AP of 0.94.