## 4 The Application of CNN and Other Deep Learning Algorithms
Compared with the traditional clutter removal methods, this method has advantages in computational complexity, clutter suppression effect, and applicability [88]. In the same year, [PERSON], [PERSON] et al. innovatively adopted generative adversarial networks to enhance the synthesized GPR data and used U-Net inversion to obtain the subsurface permittivity map. In this study, GAN greatly improves the realism of synthetic radar maps, extending the proposed method to reality [89]. In 2023, [PERSON], [PERSON] et al. developed a new method for detecting underground cylindrical burial objects using A-scan images and time-frequency regression model (TFRM). In this method, the short-time Fourier transform (STFT) of one-dimensional signals is used to provide the joint distribution of time and frequency information. In this study, the data collected by CST modeling and the data measured by building the actual model were used. This method has a significant advantage in computational efficiency [90]. As the technique of GPR is developing, the deep learning framework for detection based on C-scan data has been a direction of future development. In 2019, [PERSON] et al. proposed an underground cavity detection network (UcNet) trained on 2D GPR grid images reconstructed from 3D GPR data [91]. A work from the same year designed a set of triplanar images composed of 2D images from three sections of raw GPR data as input of an AlexNet model, and the 2D images from three sections were put into RGB layers, respectively [92]. These two methods still used a 2D CNN structure while it could extract 3D features of images. Then, [PERSON] et al. developed a real 3D CNN for underground object detection, and the average classification accuracy reached 97% in the detection of underground objects with 3D GPR data collected from urban roads in Seoul [93]. Another study applied a CNN-LSTM method in the BTD problems [94]. This study compared three different structures of CNN-LSTM based on GPR B-scan image in the down-track (DT) direction, B-scan image in the cross-track (CT) direction, and a fusion of both, respectively. Moreover, this work also discussed the performance of 3D CNN architecture. The results showed that CNN-LSTM outperformed the standard CNN in terms of ROC curve. In 2021, [PERSON] et al. used 3D U-net to establish the mapping relationship between C-scan images and 3D permittivity. Using the data obtained by simulation to verify the network, the mean absolute percentage error and structural similarity are 0.0994%  
Figure 9:5 The network structure in [81]. Figure 9:6 The network structure in [84]. and 0.9980, respectively. In addition, the computational time required for this scheme to estimate the C-scan permittivity map is only 2.9 ms, which is at least 800,000 times faster than the classical reconstruction scheme [95]. In 2022, in the detection of underground defects of airport runway, [PERSON] et al. fused the 3D feature map of C-scan with the 2D feature map of top-scan. The multi-view features are combined by region alignment to complete the prediction target classification and regression. The paper is tested using a real-world runway dataset from three international airports. The test results show that the proposed method is superior to the state-of-the-art (SOTA) method and achieves a high detection rate in a variety of defect detections [96]. The network structure of [96] is shown in Figure 9:7 In the same year, [PERSON] et al. proposed a hybrid 3D electromagnetic full-wave inversion method for the reconstruction of underground detection targets. Compared with the pure FWI method BIM, the proposed method has higher accuracy and lower computational cost, while being more robust to noise for the reconstruction of multiple underground targets [97]. In 2023, [PERSON], [PERSON] et al. used YOLOv3 network with four-scale detection layer (FDL) to detect the combined image of B-scan and C-scan, from which road cracks were identified.