# A Road Defect Detection System Using Smartphones
## 3 A Proposed Road Defect Detection System
### A Road Defect Detection with 1D-Cnn
\begin{table}
\begin{tabular}{c c c} \hline
**Experiment Environment** & **Speed Bump** & **Manhole** & **Pothole** \\ \hline Front Dashcam (Day) & 98.4\% & 94.8\% & 92.3\% \\ Rear Dashcam (Day) & 82.6\% & 68.3\% & 61.8\% \\ Front Dashcam (Night) & 78.3\% & 52.5\% & 43.8\% \\ \hline \end{tabular}
\end{table} 
Table 5: The ratio of discarded data by the label quality verifier. ### Evaluation Results of the RDD-CNN

In this subsection, we evaluate the performance of road defect detection models. The performance of the RDD-CNN model trained on manually generated data by humans versus data produced by automatic data collection is evaluated. As shown in Table 6, the performance evaluation revealed a negligible error rate of 1%. The model trained on automatically generated data showed slightly lower accuracy due to a reduced number of data points, resulting from the system discarding certain data. The manually collected dataset comprised a total of 1287 data, while the automatically generated dataset contained 1137 data. As the volume of collected data increases, this disparity is expected to decrease. Table 7 illustrates the impact of a smartphone's placement within a vehicle on its accuracy in detecting road defects. The results indicate that there was no discernible difference in accuracy when the smartphone was positioned either on the dashboard or the passenger seat, likely due to the stability provided in these locations. However, the accuracy diminished when the smartphone was placed in less stable locations, such as the cup holder, the door pocket, or the clothes pocket. In these positions, the smartphone experienced more movement, especially when the vehicle traversed road imperfections, leading to increased errors in data collection and, consequently, a reduction in accuracy. This variation in data accuracy based on the smartphone's location within the vehicle underscores the importance of a stable mounting position for optimal data collection, particularly when utilizing smartphones for detecting road defects. Future research is needed to ensure consistent accuracy regardless of smartphone placement. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Types of Training Datasets** & **Speed Bump** & **Manhole** & **Pothole** \\ \hline Manually generated data & 99.00\% & 88.46\% & 87.29\% \\ Automatically generated data & 99.00\% & 87.54\% & 86.77\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance of RDD-CNN model according to types of training datasets. Figure 6: Environmental factors obstructing video image classification: (**a**) when excessive light enters the camera; (**b**) when excessive light enters a road defect; and (**c**) when another vehicle obscures a road defect. Figure 7 presents a confusion matrix that provides a detailed view of the performance of the SVM model, Random Forest model, LSTM model, and RDD-CNN model when utilizing automatically generated. The results indicate that RDD-CNN is exceptionally adept at distinguishing between speed bumps and no-defect scenarios, achieving an accuracy of 99%. This precise discrimination is largely due to the significant variance in vehicle motion induced by speed bumps as opposed to other defect types. On the other hand, potholes and manholes, which are generally characterized by their recessed shapes, offer a more subtle challenge for distinction, hinging on the depth and shape of the depression. Manholes usually feature a relatively even surface, whereas potholes are characterized by more pronounced irregularities and indentations, as shown in Figure 2. The similarity in the physical characteristics of potholes and manholes leads to a comparatively lower performance in the classification of these two types of road defects. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Dashboard** & **Passenger Seat** & **Cup Holder** & **Door Pocket** & **Clothes Pocket** \\ \hline Accuracy & 91.10\% & 90.76\% & 84.61\% & 83.07\% & 82.85\% \\ \hline \hline \end{tabular}
\end{table}