# Rebar Recognition Using Multi-Hyperbolic Attention in Faster R-CNN
## 5 Discussion and Conclusions  
This study employs a Faster R-CNN object detection algorithm based on a multi-hyperbolic attention mechanism to locate and identify the rebar hyperbolic features within ground-penetrating radar images. Experimental validation was conducted using a ground-penetrating radar image dataset constructed from a simulated tunnel lining public dataset and real data from the Husa Tunnel. The following are the conclusions drawn from this research:  
(1) By refining the feature extraction module to focus the model's attention on hyperbolic patterns, the model's \(AP\) improved by 1.37%, enhancing its capability to recognize hyperbolic features associated with rebar.  
(2) Incorporating hyperbolic distance into the model enables more effective differentiation of hyperbolic patterns across various targets. This adjustment increased the model's \(AP\) by 1.82%, further enhancing its ability to isolate rebar-specific hyperbolic patterns within complex image data.  
(3) In this model, attention is first directed toward hyperbolic features, followed by the integration of hyperbolic distance. This combined approach resulted in an overall \(AP\) increase of 3.14%, providing a hyperbolic feature-focused method for rebar detection in tunnel structures.  
(4) Reducing the regression loss weight while magnifying the classification loss weight results in a 1.41% increase in the model's AP, demonstrating an improved recognition of rebar hyperbolas.  
Although the proposed model exhibited promising performance in this study, the varied real-world conditions of tunnels, including differences in shape, materials, and noise levels in radar images, could substantially impact its effectiveness. Consequently, the model might benefit from a more diverse dataset to enhance its adaptability to different envi  
Figure 6: Comparison of ablation experiment results (**a–c**): Baseline model detection results; (**d–f**): Ours Faster R-CNN detection results).  
ronmental conditions. Moreover, given that the training time for this model exceeds that of single-stage models, practical implementation must consider computational resources and time limitations. Future research could explore the use of three-dimensional ground-penetrating radar to gather reflection data from various tunnel linings, thus improving the accuracy of identifying embedded objects, such as rebar. Additionally, integrating advanced data processing algorithms and deep learning techniques to minimize computational costs and boost efficiency could enable future research to achieve intelligent recognition and automated analysis in complex underground settings. These advancements would greatly facilitate real-time monitoring of tunnel health, allowing for the prompt identification of potential issues and ensuring the safe operation of these critical infrastructures.  
Conceptualization, C.L. and N.C.; methodology, C.L., N.C. and T.P; software, N.C.; validation, X.Y. and H.L.; formal analysis, N.C.; investigation, X.Y.; data curation, L.W., H.L. and X.Y.; writing--original draft preparation, N.C.; writing--review and editing, C.L.; project administration, C.L. and L.W.; funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript.  
This work is funded by the National Natural Science Foundation of China (Grant No. 62263015) and supported by the Yunnan Provincial Key R&D Program (Grant NO. 202203 AA080006).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
Author [PERSON] and [PERSON] was employed by the company Yunnan Aerospace Engineering Geophysical Detecting Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.