# A Road Defect Detection System Using Smartphones
## 3 A Proposed Road Defect Detection System
### A Road Defect Detection with 1D-Cnn
These findings indicate that vision-based road defect detection techniques are highly sensitive to environmental conditions, rendering them unsuitable for reliable detection. The accuracy of road defect classification from data collected by the front dashcam during the day is, on average, 24.27% higher than that of the rear dashcam. This decline is primarily due to the lower image quality of the rear dashcam and the obstruction caused by the defroster lines on the vehicle's rear window. Therefore, the proposed automatic data collection mechanism limits the collection of raw data to daytime conditions using video from front dashcams. This approach is adopted to enhance the reliability and quality of the generation data. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Speed Bump** & **Manhole** & **Pothole** \\ \hline Ratio of discarded & 2.483\% & 4.787\% & 5.817\% \\ data & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The ratio of discarded data by the threshold-based data classification. Figure 5: YOLOv5m-based road defect classification samples. In the data labeling phase, testing a single video image with the YOLOv5m model yields an average accuracy of 95.17%. Although this accuracy rate is relatively high, it is still considered insufficient for use as a label in training data. To overcome this limitation, we implemented the label quality verifier technique, which assigns a single label based on aggregated results from multiple video image analyses. This method ensures that a label is adopted only if the consensus among the results surpasses a classification threshold. Any dataset not meeting this threshold is subsequently discarded. Table 4 presents the experimental results on the proportion of data discarded and the accuracy of labels according to classification thresholds. As the classification threshold increases, the amount of discarded data also increases, but this leads to higher label accuracy, thereby generating more reliable data. To maximize the collection of accurate data, we utilize data defined with a 90% classification threshold for training the deep learning model. Upon analyzing the images of discarded data, it was discovered that a significant number of instances failed to be classified by the YOLOv5m model for environmental reasons depicted in Figure 6. In the case of speed bumps, their extensive coverage area resulted in fewer instances being obscured by light effects or obstacles. Conversely, manholes and potholes, characterized by smaller areas of defect, were more frequently affected by light and obstacles, leading to an increased rate of data exclusion. Beyond environmental factors, the YOLOv5m model exhibited lower classification accuracy for manholes and potholes compared to speed bumps, contributing to a higher proportion of data being discarded due to the label quality verifier's criteria. Due to these reasons, as indicated in Table 5, with a 90% classification threshold set by the label quality verifier, 13 out of 589 data points for speed bumps, 68 out of 358 data points for manholes, and 69 out of 340 data points for potholes were discarded. \begin{table}
\begin{tabular}{c c c} \hline
**Classification Thresholds** & **Ratio of Discarded Data** & **Label Accuracy** \\ \hline
100\% & 14.60\% & 100\% \\
90\% & 11.65\% & 100\% \\
80\% & 9.86\% & 98\% \\
70\% & 4.21\% & 96\% \\
60\% & 3.03\% & 95\% \\ \hline \end{tabular}
\end{table} 
Table 4: The ratio of discarded data and the label accuracy according to classification thresholds by the label quality verifier. \begin{table}
\begin{tabular}{c 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 3: Performance comparison of YOLOv5m-based road defect classification in various environments.