## 5 Conclusions  
In this paper, a novel framework is proposed for slab arching detection using track geometry data. Based on human vision simulation, the method converts the original series data into 2D images and utilizes CNN for feature extraction and arching detection. The main conclusions are as follows.  
1. The alignment algorithm combining correlation analysis and DTW can correct the milepost deviation of different inspections effectively. Based on the aligned data, it is found that the longitudinal level irregularities can reflect slab arching, and the data wavelengths are close to the length of the track slab.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**PDI** & **Subset 1** & **Subset 2** & **Subset 3** & **Subset 4** & **Subset 5** \\ \hline F\({}_{1}\)-score & 1.5 & 0.7 & 1.5 & 3.4 & 1.5 \\ Time/Epoch & 0.6 & 0.6 & 0.6 & 0.6 & 0.6 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance improvement of the proposed model compared with the DNN model.  
Figure 13: Examples of detected and undetected arching images in the testing set using the proposed model. (**a**) Examples of correctly detected arching images that cannot be detected by DNN; (**b**) Examples of undetected arching images.  
2. The proposed detection framework can accurately detect arching damage, whose Precision, Recall, and F\({}_{1}\)-score can reach 98.3%, 98.4%, and 98.4%, respectively. Moreover, the data augmentation operation and balanced set establishment can help the model extract arching features more adequately and improve the performance. As the pattern ratio changes from 6:1 to 1:1, the F\({}_{1}\)-score can increase from 92.5% to 98.0%.
3. The proposed framework outperforms the plain DNN model, showing the excellent spatial characteristics learning ability. Compared to the plain DNN, the F\({}_{1}\)-score of the proposed model increases up to 3.4 times, and the training time is reduced by 0.6.  
In practice, engineers can use the proposed vision-based method to accurately detect slab arching, quickly locate the damage sections, and promote the achievement of intelligent maintenance decisions. Additionally, the method can be expanded to other damage detection of track structures based on series data.  
Conceptualization, L.G. and Z.M.; methodology, Z.M.; software, Z.M. and S.M.; validation, Z.M.; formal analysis, Z.M.; investigation, Z.M., Y.Z. and B.A.; resources, L.G., Y.Z. and S.M.; data curation, S.M. and Z.M.; writing--original draft preparation, Z.M.; writing--review and editing, B.A., Y.Z., S.M., L.G. and Z.M.; visualization, Z.M. and B.A.; supervision, L.G. and Y.Z.; project administration, L.G.; funding acquisition, L.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key R&D Program of China, grant number 2017 YFB1201104, and National Natural Science Foundation of China, grant number U1734206, 51827813, and 51908031.  
The authors thank the Infrastructure Inspection Institute of China Academy of Railway Sciences Corporation Limited for providing the data used in this research work.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.