# Speed Bump and Pothole Detection Using Deep Neural Network with Images Captured through ZED Camera
## 5 Conclusions  
A deep neural network for the detection of speed bumps and potholes was successfully designed using images captured by a ZED stereoscopic camera placed on the front of an SUV. This system has the potential to improve the safety of automated and human drivers. The high degree of precision achieved during the validation process is satisfactory, and the database is unique for capturing the specific situation of the streets of Celaya, Guanajuato, Mexico. The deep convolutional network architecture only consists of seven layers, which makes it light to implement in embedded and high-performance computing systems. The proposed methodology takes advantage of all the information coming from the camera  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Filter Size** & **Filter Quantity** & **Accuracy** & **Training and Validation Time** \\ \hline
1 & \(5\times 5\) & 36 & 91.12\% & 17 min 27 s \\
2 & \(5\times 5\) & 37 & 92.06\% & 17 min 46 s \\
3 & \(5\times 5\) & 38 & 64.95\% & 20 min 33 s \\
4 & \(7\times 7\) & 37 & 57.94\% & 20 min 56 s \\
5 & \(3\times 3\) & 37 & 98.13\% & 17 min 21 s \\
6 & \(3\times 3\) & 38 & 83.18\% & 17 min 46 s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracies of different convolutional network architectures.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Dataset Size** & **Accuracy** & **Detected Anomalies** \\ \hline [PERSON] et al. [9] & 9053 images & 77\% & Potholes \\ [PERSON] et al. [22] & 665 images & 95\% & Potholes \\ Our proposal & 714 images & 98.13\% & Potholes and Speed bumps \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of different proposals of the state of the art.  
at its maximum resolution because no stride or pooling process is performed. Compared to other larger architectures, better results were achieved with fewer flops. Unlike other architectures, ours can classify potholes and speed bumps. As future work, we aim to increase the size of the database to include special cases and to implement this method in an integrated system, making it easily accessible for use on board cars. Another convenient future work is to implement the architecture parallelization using GPU and/or FPGA hardware. We believe that this system has great potential for further development and can make a significant contribution to improving road safety.  
Conceptualization, A.-I.B.-G. and J.-E.P.-L.; methodology, J.-A.P.-M.; software, J.-A.M.-V.; validation, D.L.-M. and M.-J.V.-A.; formal analysis, F.-J.P.-P.; resources, J.P.-O.; data curation, D.L.-M.; writing--original draft preparation, J.-E.P.-L.; writing--review and editing, J.-A.M.-V. and J.-J.-M.-N.; visualization, F.-J.P.-P.; supervision, J.-A.P.-M.; project administration, A.-I.B.-G.; funding acquisition, A.-I.B.-G. All authors have read and agreed to the published version of the manuscript. This research was funded by CONACYT in Becas Nacionales and Sistema Nacional de Investigadores grants. A part of the APC was funded by TecNM en Celaya.  
The authors declare no conflict of interest.