## 6 Conclusions
The results of the case study showed that the optimization process using DARTS required 83.6% less time and improved the highest comprehensive performance (\(F_{2}^{\text{TGD}}\)) by 6.7% compared to a black-box acquired model under the conditions of using the same dataset and computing resources. (4) Two-dimensional convolution was more suitable than one-dimensional convolution for processing CVD to identify TGDs. The comprehensive performance (\(F_{2}^{\text{TGD}}\)) of the model built with 2D convolution was 33.3% higher than that of the model built with 1D convolution for identifying LLs. (5) \(F_{2}^{\text{TGD}}\) could effectively evaluate the comprehensive performance of the model based on the field requirements of metro track infrastructure management. As indicated by the results of the case study, when evaluating different performance models, the model that satisfied the lower missed-detection case had a higher \(F_{2}^{\text{TGD}}\). With the proposed method, metro track infrastructure managers can efficiently evaluate the metro track condition via portable detectors. At the same time, considering the similarity between metro tracks and railway tracks, the proposed method can be further extended to the detection of TGDs in conventional and high-speed railway tracks to improve the efficiency of infrastructure maintenance, but more data still need to be collected to verify the feasibility. However, this study had certain limitations. First, the proposed model was only used to validate and discuss the identification performance of the LLs of metro tracks at present. Second, this study assumed that only one TGD was present within a small distance (20 m in this study); thus, the proposed model needs to be further expanded for the special situation featuring multiple defects within 20 m. Finally, the actual data used in this study were limited in scope. To address these shortcomings, further research should focus on the applicability of other types of TGD. Additionally, sufficient data should be acquired to validate the model and further verify the model's ability to handle actual situations. Conceptualization, Z.W. and R.L. ; methodology, Z.W. and R.L. ; software, Z.W. ; validation, Y.G. and Y.T. ; data curation, Z.W. and Y.G. ; writing--original draft preparation, Z.W. ; writing--review and editing, R.L., Y.G. and Y.T. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (NSFC) under Grant 62132003, 71801010, and the Science and Technology Research and Development Plan Project of China State Railway Group Co., Ltd. under Grand (Temp-37). Not applicable. The dataset used in this study was available from a public data repository. The use of data follows the statement file in the repository: [[https://github.com/Elscip/scidata_jrrt_1](https://github.com/Elscip/scidata_jrrt_1)]([https://github.com/Elscip/scidata_jrrt_1](https://github.com/Elscip/scidata_jrrt_1)). Access date: 30 March 2022. The authors declare no conflict of interest. * [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON] [PERSON] Smartphone's Sensing Capabilities for On-Board Railway Track Monitoring: Structural Performance and Geometrical Degradation Assessment. _Adv. Civ. Eng._**2019**, _2019_, 1729153. [CrossRef]
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