# A Cloud-Based Ambulance Detection System Using YOLOv8 for Minimizing Ambulance Response Time
## 5 Experimental Results
### Comparison between YOLOv5, YOLOv7, and YOLOv8  
To compare our proposed YOLOv8 model with other models presented in the literature [33; 34], we conducted another two experiments for YOLOv5 and YOLOv7 that represent all of the countries that we have listed (i.e., the universal model) in two different scenarios including pre-trained and non-pre-trained. Figure 10 depicts the results of accuracy for YOLOv5 and YOLOv7. We can observe that both models show slightly lower accuracy scores comparatively with YOLOv8 (see Figure 8a). YOLOv7 has achieved 0.953 for the pre-trained scenario and 0.978 for the non-pre-trained scenario, while YOLOv5 has achieved 0.979 for the pre-trained scenario and 0.975 for the non-pre-trained scenario. Meanwhile, YOLOv8 has scored 0.982 and 0.980 for the pre-trained and non-pre-trained scenarios, respectively. Figure 10: Universal YOLOv5 vs. YOLOv7 accuracy. (**a**): Accuracy of YOLOv5. (**b**): Accuracy of YOLOv7. Figure 9: The universal model confusion matrix. Figure 11 illustrates the results of precision for YOLOv5 and YOLOv7. YOLOv5 surpasses YOLOv7, with both pre-trained (0.960) and non-pre-trained (0.953) scenarios exhibiting higher precision. However, YOLOv8 (see Figure 8b) has recorded an even higher precision rate than both YOLOv5 and YOLOv7, scoring 0.976 for the pre-trained scenario and 0.963 for the non-pre-trained scenario. Figure 12 shows the results of recall for YOLOv5 and YOLOv7. YOLOv5 has achieved 0.951 for the pre-trained scenario and 0.938 for the non-pre-trained scenario. In addition, YOLOv7 has achieved 0.914 for the pre-trained scenario and 0.940 for the non-pre-trained scenario. On the other hand, YOLOv8 (see Figure 8c) has overcome both YOLOv5 and YOLOv7 models in recall, scoring 0.958 for the pre-trained scenario and 0.951 for the non-pre-trained scenario. Figure 13 depicts the results of the F1-score for YOLOv5 and YOLOv7. YOLOv5 has achieved a slightly higher F1-score for pre-trained models (0.955) compared to YOLOv7 (0.919). Meanwhile, YOLOv8 (see Figure 8d) has scored 0.967 and 0.957 for pre-trained and non-pre-trained scenarios, respectively. In summary, YOLOv8 emerges as the top performer among the three versions, offering the highest accuracy, precision, recall, and F1-score for both pre-trained and non-pre-trained models. Having carefully evaluated the performance metrics, we elected to proceed with YOLOv8 as our preferred choice. This decision stems from a thorough examination of various factors, including computational resources and implementation constraints, ensuring alignment with our specific application requirements. Table 5 shows the analysis of YOLOv5, YOLOv7, and YOLOv8, based on the empirical data, shedding light on their performance differences in object detection tasks. YOLOv8 stands out with robust performance, achieving high accuracy scores of 0.982 for  
Figure 11: Universal YOLOv5 vs. YOLOv7 precision. (**a**): Precision of YOLOv5. (**b**): Precision of YOLOv7. Figure 12: Universal YOLOv5 vs. YOLOv7 recall. (**a**): Recall of YOLOv5. (**b**): Recall of YOLOv7. pre-trained models and 0.980 for non-pre-trained scenarios. YOLOv8 demonstrates high precision scores for both pre-trained (0.976) and non-pre-trained (0.963) models. Additionally, YOLOv8 recall values are commendable, with pre-trained models achieving 0.958 and non-pre-trained models achieving 0.951, resulting in an impressive F1-score of 0.967 and 0.957 for pre-trained and non-pre-trained models, respectively. In light of our observation, the YOLOv7 pre-trained model did not outperform the non-pre-trained one, and the reason behind that could be attributed to its ability to adapt more effectively to the specific characteristics of the dataset over time. While the pre-trained model initially demonstrates better performance due to its transfer learning from a general dataset, it might struggle to fully adapt to the nuances of the target domain as training progresses.