## 3 Results and Discussion
### Experiments of Object Detection Algorithm  
To thoroughly demonstrate the effectiveness of the improved method, we meticulously selected half of the MOT17 [19] dataset and half of the MOT20 [20] dataset, supplemented with a custom panoramic dataset, for training over 100 epochs. This combination of data not only covers a variety of scenes, but also ensures the comprehensiveness of the testing. This study conducted training sessions for both the original and optimized models, followed by a detailed performance comparison based on a series of quantitative metrics. This process is aimed at visually presenting the specific contributions of improvements to enhancing model performance. The comparative results are illustrated in Figure 6.  
As illustrated in Figure 6, the blue curve represents the training results of the original YOLOX detector, while the orange curve showcases improved detector performance throughout the training process. By comparing these two curves, we can clearly observe that, although the proposed model exhibits a slightly slower fitting speed during training, it achieves a significant improvement in accuracy compared to the original model. The related quantitative metrics are detailed in Table 3 for a more in-depth performance analysis.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hardware Name** & **Hardware Configuration** \\ \hline CPU & Intel(R)Core(TM)i7-10700K CPU 3.80 GHz \\ GPU & NVIDIA GeForce RTX 2080 ti \\ RAM & 16 GB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hardware system environment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Software Environment** & **Descriptive** & **Version** \\
**System** & **Descriptive** & Windows11 \\ \hline Operating system & Operating system & Windows11 \\ Anaconda3 & Package environment manager & 2.3.1 \\ torch & Deep learning framework & 2.0.0 \\ torchvision & Computer vision graphics library & 0.15.0 \\ Python & Programming language environment & 3.8.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Software environment system.  
To conduct a more thorough performance analysis, Table 3 compares the quantitative metrics of two detectors after training. Observing the results in the table, it is evident that, after improvements, the detector's AP50 metric increased by three percentage points, and the AP50_95 metric increased by two percentage points, providing a strong foundation for the subsequent tracker. AP50 refers to the Average Precision (AP) when the Intersection Over Union (IOU) threshold is set at 50%. AP50_95, on the other hand, refers to the Average Precision calculated by averaging the AP values obtained across a range of IOU thresholds from 50% to 95%. This approach gives a broader evaluation of the detector's performance across varying degrees of overlap between the predicted and ground-truth bounding boxes.  
This comparison highlights the effectiveness of improvements in enhancing the detector's accuracy. The slower fitting speed of the proposed model could be attributed to the additional complexities introduced by modifications, such as more sophisticated attention mechanisms or enhanced feature extraction layers. However, these enhancements contribute to a more robust model capable of achieving higher precision in object detection tasks, particularly in challenging environments or scenarios presented by the datasets used.