## 3 Experiment and Results
### Ablation Experiment  
An ablation experiment involves systematically removing or modifying individual components or parts of a model in order to assess their contribution to the overall performance. To accomplish this, we used the same dataset, hyperparameters, and training/testing procedures for all models, with the exception of the specific components being evaluated, throughout the study. This enabled us to attribute any differences in performance between the modified model and the original model to the specific modifications under consideration.  
The ablation experiments are presented in Table 3. The results of the ablation study showed that the changes made to the YOLOv5 model had a significant impact on the accuracy and [EMAIL_ADDRESS] performance metrics. When compared to the original YOLOv5, the YOLOv5-Slim model had a slightly lower precision but a higher [EMAIL_ADDRESS]. The addition of the ghost module to the YOLOv5-Slim-Ghost model improved the [EMAIL_ADDRESS] score, while keeping the precision score relatively high. This demonstrates that incorporating a ghost module can be an effective way to reduce the computational cost while maintaining the accuracy and [EMAIL_ADDRESS]. The YOLOv5-Slim-Ghost-SimSPPF (Improved-YOLOv5) model, which combined the previous modifications with a Simplified Spatial Pyramid Fusion module, outperformed the previous models in terms of the precision and [EMAIL_ADDRESS], while maintaining a similar recall score. This suggests that the SimSPPF module can significantly improve the model's performance, particularly in terms of the accuracy and [EMAIL_ADDRESS].  
The ablation study demonstrated a clear tradeoff between the number of parameters and the computational cost, as measured by GFLOPs. The YOLOv5 model had the most  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Parameters** & **GFLOPs** & **Weight/MB** \\ \hline Yolov5n & 1,763,224 & 4.1 & 3.72 MB \\ Improved-YOLOv5 & 717,020 & 2.1 & 1.76 MB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the models’ complexity.  
parameters (1,763,224) and GFLOPs (4.1), respectively. The YOLOv5-Slim-Ghost model, on the other hand, had the fewest parameters and GFLOPs, with 716,636 and 2.1, respectively. This suggests that combining the slim and ghost modules can result in a more efficient model with a significantly lower computational cost, while still maintaining relatively good performance. However, the addition of the SimSPPF module in the Improved-YOLOv5 model increased the number of parameters and GFLOPs over the YOLOv5-Slim-Ghost model. The Improved-YOLOv5 model had 717,020 parameters and 2.1 GFLOPs, which was comparable to the YOLOv5-Slim-Ghost model. This implies that the addition of the SimSPPF module can improve the model performance without significantly increasing the computational cost. Overall, the ablation study results indicated that the changes made to the YOLOv5 model led to more efficient and effective bus detection models. The slim and ghost modules, which reduced the parameters, significantly reduced the computational cost while maintaining relatively good performance. The SimSPPF module improved the model's performance, particularly in terms of the precision, without significantly increasing the computational cost.