## 7 Conclusions and Future Work  
This work aims to contribute positively by assisting the Ministry of Transport, especially the municipalities in the Kingdom of Saudi Arabia, use modern technologies such as artificial intelligence by developing an efficient automated system that can detect road damage. A method for classifying road damage based on deep learning algorithms was proposed and trained with four CNN models on a reliable Saudi dataset. Based on the size of the obtained dataset, the transfer learning approach was used with three pre-trained models to solve the problem of data volume and training time. Customized FC layers  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Training Time** \\ \hline RoadNet (CNN) & 97\% & 0.97 & 0.96 & 0.96 & 11 m, 25 s \\ \hline AlexNet & 96.5\% & 0.97 & 0.96 & 0.96 & 35 m, 12 s \\ \hline VGG-16 & 94.3\% & 0.94 & 0.94 & 0.94 & 3 h, 25 m, 15 s \\ \hline ResNet-34 & 81.7\% & 0.86 & 0.81 & 0.81 & 16 m, 55 s \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison between RoadNet, AlexNet, VGG-16, and ResNet-34 to classify road damage severity based on six different types of surface damage using our dataset.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multirow{2}{*}{**Research**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Classes**} & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Evaluation Metrics**} \\ \cline{5-6}  & & & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline [PERSON] et al. [PERSON] et al. (2018) & 500 & 2 & & N/ A & 0.86 & 0.92 & 0.89 \\ \hline Our work & 242 & 6 & & 98.6\% & 0.99 & 0.99 & 0.99 \\ \hline Silva and Lucena Silva and Lucena (2018) & 851 & 2 & & 92\% & N/ A & N/ A & N/ A \\ \hline Our work & 242 & 6 & & 94.3\% & 0.94 & 0.94 & 0.94 \\ \hline [PERSON] et al. [PERSON] et al. (2017) & 2173 & 2 & & 94\% & 0.88 & N/ A & N/ A \\ \hline Our work & 242 & 6 & & 98.4\% & 0.99 & 0.98 & **0.98** \\ \hline \hline \end{were defined and associated with the pre-trained models to perform classification. Five classification evaluation metrics, were used to evaluate the methods. The results of classifying six types of damage show that RoadNet obtained the highest accuracy at 98.6%, followed by AlexNet at 98.4%, VGG-16 at 95.1%, and Resnet-34 at 80%. According to the severity of the damage, RoadNet achieved a better accuracy of 97% than other pre-trained models. One current limitation of our models is that they are calibrated using a relatively limited dataset. The performance of the models will be improved by having more data available to cover all types of potential damage. In future work, the proposed dataset will be improved by adding more images from other regions of the Kingdom of Saudi Arabia and using the dash-cam and smartphones. It is hoped that collaboration with research groups and municipalities in Saudi Arabia will lead to a much more extensive database for a more accurate predictive model. There will also be work to develop and improve our model to aid the surveillance cameras (CCTV) on roads in detecting damages in real time and informing the responsible authorities of the location and type of damage to improve decision making. Future work also includes using validation methods such as k-fold cross-validation to estimate the models' performance and evade bias and over-fitting during the training process.  
All authors have contributed equally to this work. All authors have read and agreed to the published version of the manuscript.  
The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code: (20 UQU0067 DSR).  
Not applicable.  
The authors would like to thank the municipality of Rabigh Governorate, Saudi Arabia, especially Eng. [PERSON], Head of Projects and Studies Department at Municipality of Rabigh Governorate and Eng. [PERSON] for providing their expertise.  
The authors declare no conflict of interest.