## 4 The Application of CNN and Other Deep Learning Algorithms
The feasibility of the method is verified in the actual data monitoring [76]. In 2023, [PERSON], [PERSON] et al. used Mask R-CNN to detect vertical cracks in asphalt pavement and measure the crack width. The average error of the width measurement is only 2.33% [77]. Figure 9:2 The network structure in [75]. There were other deep learning algorithms applied to GPR detection and which achieved good performance. In 2014, a study utilized a deep belief network (DBN), which can extract large, noisy, and complex datasets into compact feature sets, for detecting BEHs and achieved 91% probability of detection with 1.4 false alarms per square meter [78]. In 2022, DBN was also used to study factors affecting ground-penetrating radar signals when detecting reinforcement in concrete structures [79]. Another landmine detection adopted a convolutional autoencoder to find anomalous signatures [80]. In addition, without data of landmine reflection in the training set, this method could avoid missing novel landmine models. SSD is another important deep learning model that can produce fixed-size bounding boxes and score for the possibility of the target's presence. It was used in the detection and localization of rebar in concrete and proven to be seven times faster than Faster-RCNN with comparable ROC curves [81]. The network structure of [81] is shown in Figure 9:3 If we improve on the basis of SSD. Adding the feature pyramid network (FPN) to the whole network and using generalized intersection over union (GIoU) loss as the loss function can further improve the performance of the network [82]. Long short-term memory (LSTM) network has also been applied to GPR signal processing. In 2020, CNN-LSTM was also applied to the detection of underground cylindrical objects and outperformed single CNN or single LSTM [83]. In 2021, [PERSON] applied the residual CNN + Bi-LSTM model to the analysis of GPR B-scan images. This model is capable of automatically predicting GPR type, soil type, and scan frequency with high accuracy and fewer parameters, which has the potential for improvement [84]. The network structure of [84] is shown in Figure 9:4 In the same year, [PERSON], [PERSON] et al. used LSTM to detect radar wavelets in the region that were used in traditional clutter-filtering algorithms [85]. In 2022, [PERSON] et al. also combined the convolutional neural network based on DenseUnet and the recurrent neural network based on bidirectional convolutional long short-term memory to establish a new network model. The network performs both inversion and object recognition. Since the features of these two tasks are highly shared, the computational complexity can be greatly reduced. This paper validates the model at two levels. Synthetic data based on defect models are used first, and then sandbox models are exploited for testing in reality. The proposed model achieves good results on both datasets [86]. In recent years, generative adversarial networks (GAN) have also been gradually applied to GPR image detection. In 2021, [PERSON] et al. used an improved least squares generative adversarial network to generate GPR images to solve the problem of insufficient data. Compared with DCGAN and LSGAN, the images generated by the improved LSGAN are clear, and can learn hyperbolic characteristics better and show more details [87]. In 2022, [PERSON] et al. used conditional generative adversarial networks (cGANs) to complete clutter suppression of GPR images. In this method, the generation network processes the cluttered image into a clutter-free image, while the detection network takes the paired cluttered and clutter-free data as input and determines whether the clutter-free data are from the generation network. Since the network needs pairs of clutter-free images and clutter-free images as input and the real clutter-free image is difficult to obtain, the clutter image is obtained by superadding the simulated target image and the real clutter. The trained model produces good results on both simulated and real datasets.