###### Abstract
[1] have proved the feasibility of using FBGs to detect cracks of different diameters on the rail tracks, by comparing the energy attenuation of the guided waves signals. In this paper, an image classification approach is proposed, where the feature extraction process is simplified using a deep neural network (DNN). In recent years, deep learning has been introduced to structural health monitoring (SHM) [27]. [PERSON] et al. [28] gave an overview of the use of data-driven deep learning strategies in ultrasound systems, ranging from the front-end to advanced applications. [PERSON] et al. [29] proposed a DNN for sequence analysis, which greatly improved the data acquisition efficiency on structural NDT using ultrasonic B-scan. It has been proven that DNNs can improve the efficiency and accuracy of the NDT using ultrasonic devices. Moreover, DNNs specially designed for UGW testing were proposed, which further outperform widely used conventional analysis methods [30; 31; 32]. These applications are all based on developing innovative networks to process the ultrasonic datasets. The advantage of DNNs is that they can automatically extract features from the data collected to help identify the structural status. Instead of calculating damage indices defined by conventional statistical and analytical formulas, a robust DNN can achieve efficient classification through convolution and pooling layers, significantly simplifying the processing procedures while preventing subjective judgments [33]. Hence, deep learning has been utilized to assess the railway safety conditions. For example, [PERSON] et al. [34] utilized a pre-trained DNN to evaluate the condition of rail wheels with the input of AE data, which proved the feasibility of fusing vibration signals with audio signals for detecting damage conditions. Similar research has also been conducted, mostly using the time-series signals as the network's input. However, the influence of damage on UGW propagation will not only be observed in the time domain but also in the frequency domain. In other words, spectrograms of the UGW signals also have the potential for crack detections. In fact, such time-frequency feature representation schemes have been implemented in various research areas, such as DL-based speech processing [35], denoising for non-stationary signals [36] and emotion recognition [37]. Ultrasonic signals share homologous similarities with other voice-based datasets. Therefore, it is assumed applicable to transfer this methodology and deploy on the signal processing of UGWs. [PERSON] et al. [38] utilized spectrums of the ultrasound signals to generate damage sensitive features to conduct fault identification. It has also been proven that multiple frequency domain features, acquired by deploying continuous wavelet transform and Hilbert transform, can contribute to the DNN-based ultrasonic inspection [39]. In this paper, we make further progress based on previous research by introducing an image classification approach for feature extraction and identification of rail conditions based on UGW signals. The methodologies are firstly introduced in Section 2, where the basic physical principles of the FBG-based guided wave inspections are introduced. Then, the experimental studies are demonstrated in Section 3, including the experimental setup of the hybrid system, the rail track conditions, and the comparison of the signals obtained by both FBGs and AE sensors is also demonstrated. In Section 4, an image classifier is proposed to detect damage that exists along the propagation route of UGWs. A robust convolution neural network (CNN) is specially designed with the input being the RGB images of spectrograms of the UGW signals obtained through Short-time Fourier transform (STFT). The visualization of the extracted features is also presented in this section to show the effectiveness of the proposed method.