## 1 Introduction
So, it is difficult to achieve large-scale damage detection within the tight maintenance time; (2) although the distributed optical fiber can be laid for a relatively long distance, their installation process is complicated, and the quality of construction has a significant influence on the measurements. The laying in the ultra-long distance (e.g., whole railway line) will significantly increase monitoring costs, and the measuring accuracy cannot be guaranteed. Thus, the existing detection methods are hard to adapt to practical maintenance and cannot complete rapid and precise detection of slab arching damages. With the development of track inspection technologies and data management systems, massive track geometry data have been collected to assist maintenance. Through our analysis, track geometry data not only reflect the smoothness of rail tracks, but also contain damage information of underlying structures to be mined further. Therefore, it inspires the establishment of data-driven arching detection methods. Besides, since the track inspection is a periodical and whole line detection, the data-driven methods only need to rely on existing data and do not require extra costs, which have high economic efficiency. In the research based on track geometry data, researchers have focused more on the track quality, such as track geometry degradation [30], rail defects prediction [31], and maintenance planning optimization [32]. Track quality index (TQI) and power density spectrum are usually used to analyze the geometry data [19; 33]. The common data processing approaches include linear regression [34], Grey model [35], Bayesian model [36], time-frequency analysis [37], and machine learning [38]. A few scholars analyzed the deformation of track infrastructures by establishing data indicators. However, the methods mentioned above are relatively simple and can only extract the shallow features of the data. The arching characteristics reflected in the data are complicated and small, so the above approaches are difficult to detect slab arching damages accurately. In recent years, deep learning has received extensive attention [39]. It does not need to set quantitative indexes manually and can learn deep potential features autonomously. The popular deep learning models include deep neural network (DNN) [40], recurrent neural network (RNN) [41], convolutional neural network (CNN) [42], generative adversarial network (GAN) [43], and hybrid models combining multiple deep algorithms [44]. Besides the CNN used to process 2D images, scholars also established 1D CNN to mine the features of series data [45]. The above models have been applied to several fields of the railway domain, such as equipment fault diagnosis [46], train delay prediction [47], and railway object detection [48]. However, using deep learning to detect the slab damages is still a research gap. Due to the complexity of arching characteristics and the excellent feature extraction ability of deep learning, this paper proposes a vision-based deep framework for slab arching detection using track geometry data. Firstly, an alignment algorithm integrating correlation analysis (CA) and dynamic time warping (DTW) is established to correct mile-point error among multiple inspections, and the arching characteristics are analyzed. Then, inspired by manual vision, an arching detection method is designed based on the convolutional neural network (CNN). Before inputting to the model, the original series are converted into images by sections, and the image dataset is expanded by data augmentation. On this basis, the architecture of the framework is optimized, and the performance is evaluated on various datasets. Moreover, the proposed method is compared with the plain deep neural network. The main contribution of the study is to build an automatic detection method that can quickly and accurately detect the arching of track slabs. Compared to conventional data processing methods, the proposed method can simulate human vision and learn useful features automatically.