## 3 Experimental Design and Results
### Experimental Results and Analysis
(**d**) Pedestrian-dominant Image. All instances of pedestrians in this image are successfully detected. Pedestrian category at 88.3%. In the mAP50-90 metric, the overall performance is 66.47%, with the Car category at 74.2% and the Pedestrian category at 55.2%. This study introduced the EMA module on top of the baseline model. The improved loss function graph is shown in Figure 10.  
After the introduction of the EMA module, the loss function graph becomes noticeably smoother and more stable, with minimal fluctuations, especially when compared to the baseline model. Particularly in terms of validation loss, the incorporation of the EMA module results in a smoother trend with almost no significant fluctuations. The improved model demonstrates enhanced generalization capabilities and overall better performance. Table 4 demonstrates that the introduction of the EMA module led to significant improvements across all evaluation metrics. Specifically, the overall precision reached 93.9%, with the precision for the Car category at 94.3% and for the Pedestrian category at 93.4%. In terms of recall, the overall rate increased to 85.9%, with the Car category achieving a high recall rate of 91.5%, and the Pedestrian category reaching 80.3%. For the mAP50 metric, the overall performance was 93.8%, with the Car category reaching 96.7% and the Pedestrian category at 90.8%. Regarding the mAP50-90 metric, the overall performance was 69.1%, with the Car category at 77.9% and the Pedestrian category at 60.4%. The detection results after integrating the EMA module are shown in Figure 11. In Figure 11, the confidence scores are significantly improved, with the highest reaching 97%, demonstrating a clear enhancement in performance. In Figure 11, the improvement is more pronounced for the Car category compared to the Pedestrian category. Figure 11 shows better detection performance for close-range targets, with increased confidence scores. In Figure 11, overall detection performance is improved, with all targets showing higher confidence scores. These results indicate that the integration of the EMA module leads to a significant enhancement in the overall detection performance of the model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **Precision** & **Recall** & **mAP50** & **mAP50-90** \\ \hline All & 0.913 & 0.836 & 0.921 & 0.647 \\ Car & 0.921 & 0.901 & 0.959 & 0.742 \\ Pedestrian & 0.905 & 0.772 & 0.883 & 0.552 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Baseline model evaluation metrics.This table presents the precision, recall, and mean average precision (mAP) metrics for different classes using the baseline model. 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 10: Incorporation of EMA loss function graphs. (**a**) Training set loss. This graph shows the training loss for box, object, and class predictions over 500 epochs with the incorporation of the EMA module. (**b**) Validation set loss. This graph shows the validation loss for box, object, and class predictions over 500 epochs with the incorporation of the EMA module. To address more complex driving environments, we utilized the MPDIou loss function in the enhanced model. The improved loss function graph is depicted in Figure 12.  
After adopting the MPDIou loss function, an initial increase in some loss function values was observed, along with significant drops and fluctuations in the early stages of training. However, as the number of training iterations increased, these loss functions eventually stabilized, showing a smooth curve trend. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **Precision** & **Recall** & **mAP50** & **mAP50-90** \\ \hline All & 0.939 & 0.859 & 0.938 & 0.691 \\ Car & 0.943 & 0.915 & 0.967 & 0.779 \\ Pedestrian & 0.934 & 0.803 & 0.908 & 0.604 \\ \hline \hline \end{tabular}
\end{table}