# A Cloud-Based Ambulance Detection System Using YOLOv8 for Minimizing Ambulance Response Time
## 5 Experimental Results  
### YOLOv8 Model Performance  
For YOLOv8 model performance, we have 22 different experiments, 10 of which are pre-trained while the other 10 are non-pre-trained (i.e., two different experiments for each country). Moreover, we conducted another two experiments, which we call the universal model (i.e., pre-trained vs. non-pre-trained) for all of the countries that we have listed. Figure 3 shows how the YOLOv8 model uses the bounding box in red around the ambulance vehicles. Meanwhile, Figure 4 shows the validation of the detection of the ambulance by drawing boxes around the ambulance vehicles with an accuracy measure that states how confident the YOLOv8 model is regarding that detected ambulance. The training and running time of the proposed model are detailed in Table 3.  
The results of our experiments show promising accuracy scoring. Nonetheless, we found that some of the results took a leap down when we experimented using a non-pre-trained scenario. For example, Figure 5 illustrates the accuracy bar charts of the non-pre  
Figure 4: The validation of the detection of the ambulance vehicles. Figure 3: The bounding box in red around the labeled ambulance vehicles. trained scenario for the countries Saudi Arabia (SA), Russia (RU), Japan (JP), Germany (DE), Sweden (SE), Norway (NOR), Turkey (TR), the United Kingdom (UK), Italy (IT), and Spain (ES). In the results for the non-pre-trained scenario, we observe that SA received the highest score in accuracy, reaching 0.98. This is because of the high resolution and good angle of the captured images, as well as the large logo of the ambulance vehicles. In addition, we can see that the SE scored second (i.e., 0.976 in accuracy) because most of the types of ambulance vehicles in the SE validation set have a higher object size than others, which contributes to a higher accuracy. In other words, larger object sizes in pixels and large-size ambulances with more distinguishable features tend to yield higher accuracy, whereas smaller object sizes in pixels and medium-size ambulances with fewer distinguishable features result in lower accuracy (e.g., Sport Utility Vehicles (SUVs)); especially the YOLOv8 model works on dividing the image into an \(S\times S\) grid. On the other hand, Figure 6 illustrates the accuracy bar charts of the pre-trained scenario for the aforementioned countries. We can see that JP received the best accuracy rating of 0.995. The reasons behind this are that JP data have high resolution for the high object size in pixels in the validating set and not more than a four-view angle of the captured images, as well as the logo size and one vehicle type for the ambulance vehicles. While SE data also have a large logo of the ambulance vehicles, which will make it easier for the YOLOv8 model to distinguish this particular ambulance vehicle from other ones, the SE ambulance vehicles have only two colors as well, including yellow and green. Figure 7 depicts the results of our YOLOv8 accuracy performance for pre-trained vs. non-pre-trained scenarios among 10 different countries. Overall, we can see that the results in the pre-trained scenario overcome the non-pre-trained scenario in all countries' experiments. That is because the YOLOv8 model's first layers already knew what to look for (i.e., ambulance vehicles' shapes and colors). In the results for the pre-trained scenario, \begin{table}
\begin{tabular}{l l} \hline \hline
**Stage** & **Time** \\ \hline Pre-process & 10 ms \\ Inference & 100 ms \\ Data Transfer Time & 100 ms \\ Server Processing Time & 200 ms \\ Response Time & 310 ms \\ Data Acquisition Layer & 70 ms \\ Monitoring Layer & 30 ms \\ Ambulance Detection Layer & 110 ms \\ Cloud Layer 1 & 650 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The Training and Running Time of the Proposed Model  
Figure 5: The accuracy of the non-pre-trained model among 10 different countries. we observe that SE and RU received the best accuracy rating of 0.99 for the aforementioned reasons.