## 3 Experimental Test and Result Analysis
### Experimental Test
#### 3.4.4 Ablation Experiment  
In order to further verify the effectiveness of the improved method ROS-YOLOv5-FleetEMA model proposed in this paper, the following ablation experiments are designed: Conduct ablation experiments to explore the effectiveness of improvement methods on the model. Combine EMA, C3 Ghost, and MPDIoU with the traditional YOLOv5-Lite model in different ways. By comparing the performance of models with different configurations, ablation experiments can help us understand how each component affects the overall performance of the model, including detection accuracy, computational efficiency, and resource consumption. The ablation experiment systematically removes or replaces various components in the model, observes the impact of these changes on model performance, provides an empirical basis for model design decisions, and ensures the practicality and effectiveness of the proposed ROS-YOLOv5-FleetEMA model in resource-constrained environments. In the experimental design, \"\(-\)\" indicates that an improvement has not been applied to the model, while \"\(+\)\" indicates that the improvement has been integrated. In this way, the specific impact of each combination on the performance of the model can be clearly demonstrated. The specific results are shown in Table 6 and Figure 8.  
Through experimental analysis, the EMA attention module is introduced into the traditional YOLOv5-Lite model, and the mAP @ 0.5 is significantly improved, while the number of model parameters does not increase much. In addition, the traditional CloU loss function is replaced by the MPDIoU loss function, which further optimizes the performance of the model in terms of bounding box positioning accuracy. The MPDIoU loss function makes the model more accurate in predicting the bounding box by considering the center point and diagonal distance of the bounding box, and the predicted bounding box has a higher degree of coincidence with the real bounding box. The experimental results show that mAP @ 0.5 is increased by 0.4%, which indicates that the MPDIoU loss function can make the regression of the model to the bounding box more stable, and the prediction accuracy is higher. After the introduction of the C3 Chost module, the parameters of the model and the GFLOPS are significantly reduced while maintaining a high detection accuracy. The C3 Chost module reduces the consumption of computing resources by  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **mAP @ 0.5** & **mAP @ 0.5–0.95** \\ \hline YOLOv5-Lite & 0.765 & 0.515 \\ ROS-YOLOv5-FleetEMA & 0.792 & 0.558 \\ \hline \hline \end{tabular}
\end{table}
Table 4: mAP improvement effect comparison.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **GFLOPS** & **Param** \\ \hline YOLOv5s & 15.9 & 7,064,065 \\ YOLOv5-Lite & 3.8 & 1,566,561 \\ ROS-YOLOv5-FleetEMA & 3.3 & 1,332,471 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Lightweight improvement effect comparison.  
optimizing the feature extraction process without affecting the detection effect. Finally, all these improved methods are applied to the YOLOv5-Lite model; not only has mAP @ 0.5 been significantly improved but the number of parameters of the model has been reduced by 15.1%. This shows that these optimization strategies can significantly reduce the computational complexity and resource consumption of the model without sacrificing the detection accuracy, making the model more suitable for deployment on resource-constrained devices, such as mobile devices and embedded systems.