# A Cloud-Based Ambulance Detection System Using YOLOv8 for Minimizing Ambulance Response Time
## 6 Conclusions and Future Work  
This work presents the design and implementation of a novel ambulance detection system architecture to prioritize ambulance vehicles by turning the traffic light to green for saving patients' lives. The system is capable of handling multiple types of signal detection reports from different sources and formats by processing and storing them in the cloud. In particular, we exploit a YOLOv8 model to detect the ambulance and allow the traffic signals to turn green to speed up the ambulance's estimated arrival time at the hospital. We are the first to exploit a YOLOv8 model for the detection of ambulances, to the best of our knowledge. To demonstrate the performance of our YOLOv8 model, we collected and labeled 3000 images of ambulances from 10 different countries that cover  
Figure 13: Universal YOLOv5 vs. YOLOv7 F1-score. (**a**): F1-score of YOLOv5. (**b**): F1-score of YOLOv7.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Universal YOLOv5** & **Universal YOLOv7** & **Universal YOLOv8** \\ \hline Accuracy of pre-trained & 97.9 & 95.3 & 98.2 \\ Accuracy of non-pre-trained & 97.5 & 97.8 & 98.0 \\ Precision of pre-trained & 96.0 & 92.5 & 97.6 \\ Precision of non-pre-trained & 95.3 & 95.8 & 96.3 \\ Recall of pre-trained & 95.1 & 91.4 & 95.8 \\ Recall of non-pre-trained & 93.8 & 94.0 & 95.1 \\ F1-score of pre-trained & 95.5 & 91.9 & 96.7 \\ F1-score of non-pre-trained & 94.5 & 94.9 & 95.7 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The Universal Model Performancevarious domains and languages. The dataset can benefit the research community in training and evaluating computer vision models for cross-lingual tasks. Moreover, we conducted 22 different experiments, 10 of which are pre-trained while the other 10 are non-pre-trained (i.e., 2 different experiments for each country). 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. Furthermore, we compared the performance of our YOLOv8 model with other models presented in the literature including YOLOv5 and YOLOv7. The results of the experiments are quite promising where the universal model of YOLOv8 scored an average of 0.982, 0.976, 0.958, and 0.967 for the accuracy, precision, recall, and F1-score, respectively. The universal model scored the highest result among all countries because of the large amount of data for training where the model could understand the ambulance vehicles better. For future work, we intend to alter the network layers to suit our datasets, resulting in a high-efficiency model. In addition, we are planning to develop a new segmentation technique; semantic segmentation is the counterpart of object detection, where objects are identified with a square box, but in segmentation, objects are drawn at the pixel level.  
Conceptualization, T.H.N., A.N., A.A. and R.A.; methodology, T.H.N. and A.N.; software, A.N., T.H.N., B.A. and Z.A.; validation, T.H.N., B.A. and Z.A.; formal analysis, T.H.N. and A.N.; investigation, A.N., B.A. and Z.A.; resources, A.N. and M.A.; data curation, A.N., B.A. and Z.A.; writing--original draft preparation, T.H.N. and A.N.; writing--review and editing, A.A., R.A. and M.A.; visualization, A.N. and B.A.; supervision, A.N. and T.H.N.; project administration, A.N.; funding acquisition, A.N. and M.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data is available on the following link ([[https://github.com/basil-alharbi/Ambulance-Detection-System](https://github.com/basil-alharbi/Ambulance-Detection-System)]([https://github.com/basil-alharbi/Ambulance-Detection-System](https://github.com/basil-alharbi/Ambulance-Detection-System))) (accessed on 11 March 2024).  
The authors declare no conflicts of interest.