## 5 Conclusions  
In this paper, a novel image classification approach has been proposed to identify the rail track health status using UGW inspection. First, a hybrid sensing system composed of a PZT actuator and ultrasound FBG sensors has been proposed to conduct experiments. An artificial crack and a surface bump made of blu-tack adhesives are considered two damaged cases in the experiments, respectively. Through comparative studies between the UGW signals acquired by FBG and AE sensors, significant changes in waveforms in time and frequency domains have been observed. It has been concluded that FBGs are consummate sensors to be massively utilized in UGW inspection tasks, especially in complex environments with high-level EMI. A further contribution of this paper focuses on proposing a specially designed CNN-based approach to efficiently identify the health conditions of the rail tracks. Experimental ultrasound data under intact and defective conditions is pre-processed using STFT considering that the frequency proportion of the wave packets varies with the propagation period. The obtained spectrograms are then utilized to feed the proposed CNN model. The results have been visualized for comparison, showing high accuracy for the prediction of each rail condition. After 336 iterations, the proposed classifier achieves 98.0% overall classification accuracy with a fast convergence speed. The visualization of each convolution layer of the classifier has also been shown to illustrate the feature extraction process of the CNN.  
Consequently, the methodology proposed in this paper has the potential to be massively utilized for on-site monitoring, considering that FBGs can avoid the system disfunction caused by EMI. The image classification-based damage detection has been verified in this paper and will continue to contribute to the automatic online damage detection of railway tracks to save manual labor with less time consumption. However, for future research to achieve damage location and even quantification, more FBGs are required to be  
Figure 16: Visualization of the classification results.  
Figure 17: The feature maps of 3 channels generated by the ‘Conv1’ layer. (**a**) ‘R’ channel; (**b**) ‘G’ channel; (**c**) ‘B’ channel.  
deployed on the rail track for more-precise measurements. It is applicable to predict the approximate damage location along the railway line that lasts for kilometers if adequate measuring points are distributed. As for the determination of damage severity, more advanced damage indices will be developed to achieve the quantification.  
Conceptualization, D.-Z.D. and Y.-Q.N.; methodology, D.-Z.D. and C.-C.L.; software, D.-Z.D.; validation, D.-Z.D., C.-C.L. and Q.Z.; formal analysis, D.-Z.D. and C.-C.L.; investigation, Q.Z. and B.S.; resources, Y.-Q.N.; data curation, D.-Z.D., B.S. and Q.-F.Z.; writing--original draft preparation, D.-Z.D. and C.-C.L.; writing--review and editing, Y.-Q.N. and Q.Z.; visualization, Q.-F.Z.; supervision, Y.-Q.N.; project administration, Y.-Q.N.; funding acquisition, Y.-Q.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by a grant from the Research Grants Council of the Hong Kong Special Administrative Region (SAR), China (Grant Number: R-5020-18) and a grant from the Science, Technology and Innovation Commission of Shenzhen Municipality under Central-Guided Local Technology Development Fund (Grant Number: 2021 SZVUP143). The authors would also like to appreciate the funding support by the Innovation and Technology Commission of the Hong Kong SAR Government to the Hong Kong Branch of National Rail Transit Electrification and Automation Engineering Technology Research Center (Grant Number: K-BBY1).  
Not applicable.  
The authors declare no conflict of interest.