# A Cloud-Based Ambulance Detection System Using YOLOv8 for Minimizing Ambulance Response Time
## 5 Experimental Results
### YOLOv8 Model Performance
However, IT and NOR received the worst accuracy rating of 0.94; that is because the IT dataset has different ambulance vehicle types and colors, and the NOR dataset has low-resolution images, images taken from different angles, and medium-size ambulance vehicles (i.e., SUVs). In other words, the medium-size ambulance will have fewer boxes than the large-size ambulance (e.g., truck vehicles), which will affect the accuracy's result in a negative correlation. Figure 8 illustrates the results of our universal YOLOV8 model performance for pre-trained vs. non-pre-trained scenarios among all of the countries that we have listed. For this experiment, we trained our YOLOv8 model using 2000 images (i.e., for all ambulance vehicles in 10 different countries) and validated the universal model using 1000 images. As we can see, the universal model has scored 0.98 in accuracy (see Figure 8a) for both pre-trained and non-pre-trained scenarios, which is the highest result among all countries. The reason for that is the universal model has the largest amount of data for training where the model could understand the ambulance vehicles better. The reason for going with 100 epochs is to leave room to compare both the pre-trained and the non-pre-trained models. We can also observe that the universal model reached a precision of 0.97 and 0.96 in the pre-trained and non-pre-trained scenarios, respectively (see Figure 8b). For the recall, the universal model has scored 0.95 in both scenarios as shown in Figure 8c. Meanwhile,  
Figure 7: Pre-trained vs. non-pre-trained accuracy performance among 10 different countries. (**a**): Accuracy performance in Germany dataset. (**b**): Accuracy performance in Italy dataset. (**c**): Accuracy performance in Japan dataset. (**d**): Accuracy performance in Norway dataset. (**e**): Accuracy performance in Russia dataset. (**f**): Accuracy performance in Saudi Arabia dataset. (**g**): Accuracy performance in Spain dataset. (**h**): Accuracy performance in Sweden dataset. (**i**): Accuracy performance in Turkey dataset. (**j**): Accuracy performance in UK dataset. the universal model regarding the F1-score recorded 0.96 in the pre-trained scenario and 0.95 in the non-pre-trained scenario (see Figure 8d). More details about the accuracy, precision, recall, and F1-score results after reaching 100 epochs can be found in Table 4.  
Figure 9 shows the results of the universal model in the confusion matrix where the actual values of true and false concerning predicted values are either positive or negative. As we can see, the true positives reached 1093 when the model predicts that it is an ambulance vehicle, while the true negatives are when the model predicts that it is not an ambulance vehicle (i.e., background), which is empty in our case (i.e., because the model is not supposed to identify non-ambulances). This means that when evaluating the performance of an object detection model, the number of true negatives can easily overwhelm the number of false positives and false negatives, making it difficult to compare the performance of the models. The false positive scored 57, and it occurs when the model predicts an ambulance but it is not present. The false negative scored 39, and it occurs