## 3 Experimental Design and Results
### Experimental Results and Analysis

Table 4: Evaluation metrics for the introduced EMA module. This table presents the precision, recall, and mAP metrics for different classes after incorporating the EMA module. The results are shown for both mAP@50 and mAP@50-90 thresholds. The “All” category represents the overall performance across all classes, while specific metrics are provided for the “Car” and “Pedestrian” classes. Figure 11: Detection results with EMA integration. (**a**) Close-up view. Detection results of large targets in close-up scenarios. (**b**) Distant view. Detection results of small targets in distant scenarios. (**c**) Car-dominant image. This image predominantly contains cars, with only a few instances not being detected. (**d**) Pedestrian-dominant image. All instances of pedestrians in this image are successfully detected. Figure 12: Graphs with modified MPDIou loss function. (**a**) Training Set Loss. This graph shows the training loss for box, object, and class predictions over 500 epochs with the modified MPDIou loss function. (**b**) Validation Set Loss. This graph shows the validation loss for box, object, and class predictions over 500 epochs with the modified MPDIou loss function. Figure 13 illustrates the results after integrating the EMA module and replacing the loss function with MPDIoU. In Figure 13, the confidence scores for all detected categories exceed 90%, demonstrating better performance compared to just adding the EMA module. Similarly, in Figure 13, the confidence scores for all detected categories are above 90%, showing significant improvement. Figure 13 highlights the detection of categories that were not identified by the previous two models, with confidence scores exceeding 90% and some reaching up to 97%. In Figure 13, a slight improvement is observed, with most categories having confidence scores above 88%, except for one category at 45%. These results indicate that replacing the loss function with MPDIoU further enhances detection performance, proving the effectiveness of this improvement. Table 5 shows the changes in evaluation metrics after adopting the MPDIoU loss function. The data reveal slight improvements in certain metrics. Specifically, the overall precision was 93.7%, with the precision for the Car category at 94.5% and for the Pedestrian category at 92.8%. In terms of recall, the overall rate was 86.2%, with the Car category at 91.3% and the Pedestrian category at 81.1%. For the mAP50 metric, the overall performance was 93.6%, with the Car category at 96.4% and the Pedestrian category at 90.8%. As for the mAP50-90 metric, the overall performance was 69.5%, with the Car category remaining at 77.9% and the Pedestrian category increasing to 61.2%. After adopting the MPDIou loss function, the model experienced stable and slight improvements across various evaluation metrics. Figure 14 presents a comparison of detection results. In Figure 14, the left side shows an incorrect detection of a pedestrian where there is none, and distant vehicles are not successfully detected. Additionally, the pedestrian to the right of the red car in the middle is not detected, while a lamppost is mistakenly identified as a pedestrian, indicating mediocre detection performance. In contrast, Figure 14 corrects the errors observed in Figure 14 by  
Figure 13: Detection results with EMA integration. (**a**) Close-up view. Detection results of large targets in close-up scenarios. (**b**) Distant view. Detection results of small targets in distant scenarios. (**c**) Car-dominant image. This image predominantly contains cars, with only a few instances not being detected. (**d**) Pedestrian-dominant image. All instances of pedestrians in this image are successfully detected. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **Precision** & **Recall** & **mAP50** & **mAP50-90** \\ \hline All & 0.937 & 0.862 & 0.936 & 0.695 \\ Car & 0.945 & 0.913 & 0.964 & 0.779 \\ Pedestrian & 0.928 & 0.811 & 0.908 & 0.612 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation metrics utilizing MPDIoU.