# Recognition of Rebar in Ground-Penetrating Radar Data for the Second Lining of a Tunnel  
[PERSON]  
1 Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 6650500, China 1  
[PERSON]  
1 Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 6650500, China 1  
[PERSON]  
1 Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 6650500, China 1  
[PERSON]  
2 Yunnan Aerospace Engineering Geophysical Detecting Co., Ltd., Kunming 650200, China 2  
[PERSON]  
2 Yunnan Aerospace Engineering Geophysical Detecting Co., Ltd., Kunming 650200, China 2  
[PERSON]  
1 Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 665050, China 1  
Footnote 1: email: {x.yang,x.yang,[EMAIL_ADDRESS]  
Footnote 2: email: {x.yang,x.yang,[EMAIL_ADDRESS]  
Footnote 3: email: {x.yang,x.yang,[EMAIL_ADDRESS]  
Footnote 4: email: {x.yang,x  
results show that the accuracy of rebar recognition decreased as the frequency of GPR emission decreased. The above study revealed that as the main frequency of the GPR antenna decreases, it is more difficult to recognize the hyperbolic shape of the rebar in the B-scan. In the case of GPR inspection of a tunnel's lining structures, where the frequency of GPR emission is set below 900 Mhz, the hyperbolic shape of the rebar is not evident. Thus, it was necessary to reconstruct the hyperbolic shape of the rebar in the B-scan of the tunnel lining structure to enable recognition of the rebar. The hyperbolic shape of the rebar is an important feature for recognizing the rebar in a B-scan [6; 7; 8]. Several scholars have implemented the recognition of rebar based on the hyperbolic shape of rebar combined with image recognition. [PERSON] et al. [9] proposed the use of offset, normalized intercorrelation and thresholding to obtain the hyperbolic rebar shape in a B-scan. The rebar was then classified and identified using deep convolutional neural networks (CNNs). The experimental results show that the recognition accuracy was higher than 95.75% when applying GPR data analysis to 26 bridge decks. [PERSON] et al. [10] used the Single-Shot Multibox Detector (SSD) to obtain the B-scan's hyperbolic region of interest for rebar. The experimental results showed that the identification accuracy of 13,026 reinforcement bars was 90.9% in the B-scan image which contained 3992 images. [PERSON] et al. [11] devised a computer vision-based method for the automatic detection of rebar in complex GPR images of highly degraded concrete bridge decks. In particular, the detection model consists of a fine-tuned histogram of oriented gradient feature descriptors, a multilayer perceptron for classification and a post-processing algorithm. The model was applied to the hyperbolic region for error elimination detection and labeling of rebar. The experimental results showed an overall accuracy of 89.4% on the URIGPRv1.0 dataset. The above studies have shown that it is feasible to recognize rebar in a B-scan using the hyperbolic characteristics of the rebar. [PERSON] et al. [12] used GPR to detect rebar in columns, beams and floor slabs in order to assess the concrete structure of a building. By analyzing the B-scan diagrams obtained from the HF antenna, the amount of rebar was determined from the hyperbolic features of the diagrams. During the experiments, a reverse time migration (RTM) method was used to process the area containing the hyperbolic features of the rebar. The experimental results showed that the amount and location of the rebar could be accurately located using the RTM method based on the hyperbolic features of the rebar. Consequently, in order to recognize rebar, the first step is to figure out how the hyperbolic shape is formed. Compared with other media in the tunnel, rebar has extremely strong electromagnetic properties. The dielectric constant of rebar is considered to be infinite compared with other media [13].