# 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} \hline \hline
**Models** & **Hyperparameters** \\ \hline SVM [46] & Kernel = linear, C = 1.0, shrinking = true, tol = 0.001, random state = 0 \\ Random Forest [46] & N estimators = 10, max depth = none, min samples split = 1, random state = 0 \\  & Input shape = (300,1), learning rate = 0.001, activation = SoftMax \\  & Optimizer = Adam, loss function = categorical cross entropy, dropout = 0.5 \\  & Input shape = (300,1), activation = Swish, kernel size = 4, loss function = categorical cross entropy, optimizer = Adam \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Hyperparameters of testing models. various data inputs, it was found that each model performed an average of 533.75 tests per minute. SVM can implement more accurate models with smaller tol values but requires significantly longer processing time compared to deep learning approaches. Random Forest also shows improved accuracy with an increased number of decision trees, but at the cost of longer computation times. LSTM and RDD-CNN exhibited relatively short processing times compared to traditional machine learning techniques. Quantization did not lead to differences in accuracy but allowed for a reduction in processing time, enhancing the feasibility of using these models on smartphones. ## 5 Conclusions

This paper introduces a system that leverages smartphones to identify road defects, featuring an innovative automatic data collection mechanism and the RDD-CNN model, proficient in real-time road defect detection. This mechanism greatly facilitates the data acquisition process, enabling the automatic collection of data and label information solely

Figure 8: Accuracy of various testing models. Figure 9: Comparison of lightweight size and processing time for each model. from road driving videos and sensor data, thereby enhancing convenience in data collection. The experimental results, conducted with three smartphones in two vehicles, revealed that the automatic data collection method missed only 15.21% of the data while achieving 100% label accuracy. The testing of the RDD-CNN model, our proposed road defect classification model, with automatically collected data exhibited a negligible error margin, only 0.4% lower than that with manually collected data. The evaluation showcases the feasibility and effectiveness of employing automatically gathered data for training various AI models. Notably, the RDD-CNN model demonstrated superior classification accuracy, exceeding 86.77% compared to other models, and is optimized for smartphone use, maintaining a low processing time of approximately 0.1 s per minute of driving, confirming its real-time operational capability. The scope of this research is currently limited to classifying road defects in real-time on local smartphones. For future developments, we aim to utilize the technology to update a cloud server with real-time locations of road defects, creating a live defect map. By using crowdsourced data from the cloud server, we plan to achieve more precise road defect classifications, thus improving the overall accuracy and reliability of road defect detection and mapping, with considerations for the server's storage and bandwidth capacities. Conceptualization and methodology, G.K. and S.K. ; software, G.K.; validation, investigation, and formal analysis, G.K. and S.K. ; writing--original draft preparation, G.K.; writing--review and editing, G.K. and S.K. ; supervision, S.K. ; funding acquisition, S.K. All authors have read and agreed to the published version of the manuscript. This work was partly supported by the Innovative Human Resource Development for Local Intellectualization program through the Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korean government (MSIT) (IITP-2024-2020-0-01462, 50%). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No.