# Rebar Recognition Using Multi-Hyperbolic Attention in Faster R-CNN
## 1 Introduction  
The tunnel lining is a permanent structural component essential for maintaining tunnel stability, with rebar serving as its core material. This reinforcement plays a crucial role in ensuring the stability and safety of the tunnel. Rebar not only enhances the load-bearing capacity of the lining structure but also significantly reduces its self-weight, thereby creating a tunnel that is both lighter and structurally robust. Consequently, rebar is indispensable in tunnel construction, and its inspection plays a critical role in assessing tunnel lining quality. For scenarios where sampling and direct inspection are challenging, non-destructive testing techniques are commonly applied [1]. Ground-penetrating radar (GPR) technology, in particular, has significantly enhanced both the quality and efficiency ofengineering assessments [2; 3]. In conventional GPR-based quality inspections, hyperbolic features within radar grayscale images are manually interpreted, or alternatively, signal features are processed to facilitate rebar identification. Recently, deep learning methods [4] have gained prominence for their ability to extract detailed features from radar gray images, thus enabling accurate detection of targets within tunnel linings. The data processing method identifies rebar by utilizing raw radar data, performing data preprocessing and signal feature analysis. This approach effectively leverages the unique imaging principles of GPR and maximizes the advantages inherent in the data. [PERSON] et al. [5] proposed an algorithm based on a genetic algorithm for automatic multi-target detection in tunnel lining radar profile data. This method collects all local maxima in one-dimensional time waveform (A-Scan) GPR data to generate a binary image. It then searches for the optimal fitting hyperbola at each potential location within the binary image, matching it to the target's reflection signal according to predefined matching criteria. This approach successfully identifies rebar in both simulated and measured data. [PERSON] et al. [6] identified rebar by analyzing the propagation characteristics of electromagnetic waves within concrete. They determined the peak points associated with rebar in the one-dimensional time waveform (A-Scan). Based on these peak points, they reconstructed the hyperbolic shape of rebar in the two-dimensional time waveform (B-Scan), facilitating accurate rebar identification. The current popular deep learning approach involves feature extraction from radar-generated images, enabling the identification of targets such as rebar within tunnel linings, as well as the detection of subsurface defects. This method is highly efficient and allows for the direct processing of radar images. In recent years, deep learning-based target detection methods have emerged as a major research focus. [PERSON] et al. [7] introduced AlexNet as an effective solution for automatic rebar detection in GPR data. Compared to traditional CNNs, AlexNet demonstrates higher accuracy in identifying rebar in real-world construction scenarios. [PERSON] et al. [8] proposed a computer vision-based method for automatic rebar detection in complex GPR images of highly deteriorated concrete bridge decks. [PERSON] et al. [9] employed Faster R-CNN along with data augmentation strategies to identify hyperbolic features in GPR B-Scan images. This approach not only detects the presence of buried objects in B-Scans but also accurately identifies candidate hyperbolic regions. [PERSON] et al. [10] proposed an SSD model-based approach for the automatic detection of corrosion-prone environments in bridge deck GPR data. This method comprises data preprocessing, automatic rebar extraction, and corrosion environment mapping, significantly enhancing detection accuracy in corrosive environments. [PERSON] et al. [11] developed a Faster R-CNN-based framework for automatic recognition and tracking of highway-layer interfaces.