## Abstract
To process GPR data and automatically extract hyperbolic feature information, researchers tried to use many signal transformation and processing methods, such as the Hough transform [7; 8; 9], Radon transforms [10], and the wavelet transform [11]. With the development of machine learning, many researchers have applied machine learning methods to GPR image recognition and achieved good results. [PERSON] proposed and evaluated the method of introducing the Hidden Markov Model (HMM) into GPR data to detect mine features [12]. [PERSON] used genetic algorithms to search for hyperbolic patterns in GPR images and then used a support vector machine (SVM) classifier to evaluate the target material [13]. [PERSON] adopted an over-complete Gabor dictionary that can be dynamically refined in the process of sparse decomposition and used adaptive sparse decomposition to analyze and classify GPR data [14]. Traditional machine learning algorithms extract features through designed feature selection methods and then use classifiers to classify objects according to these features. Therefore, the key step of these algorithms is to extract feature information from images for further classification. It is very difficult to extract reliable and effective features from large quantities of complex GPR image data. With the development of machine learning technology, more and more algorithms have been proposed. Convolutional neural networks are one of the most popular algorithms. When the deep CNN trained by [PERSON] and his team in ILSVRC-2012 [15] achieved record-breaking results, it attracted the attention of researchers in different fields. Since then, CNN has achieved superior results in many fields. In the medical field, [PERSON], [PERSON] et al. combined graph convolutional networks and convolutional neural networks for breast cancer classification. Superior results are achieved compared with fifteen state-of-the-art breast cancer detection methods [16]. In the medical field, [PERSON], [PERSON] et al. combined graph convolutional networks and convolutional neural networks for breast cancer classification. In the environmental field, [PERSON], [PERSON] et al. used the CNN-LSTM model to predict the proliferation of harmful algae in Taihu Lake. It provided a new idea of scientific regulation of inland waters. Superior results were achieved compared with fifteen state-of-the-art breast cancer detection methods [17]. In the field of architecture, [PERSON], [PERSON] et al. used a 2D CNN to evaluate the torsion ability of reinforced concrete beams and used the improved bird swarm algorithm to optimize the hyperparameters. High performance was achieved [18]. To overcome the disadvantages of manual feature extraction, deep learning networks were applied to GPR target recognition. [PERSON] and [PERSON] first used the deep learning network to process GPR data and achieved 91% identification accuracy by combining supervised learning with unsupervised learning [19]. [PERSON] used transfer learning to solve the problem of GPR data samples with a small number of markers [20]. To improve the detection accuracy of GPR data, [PERSON] tried a variety of CNN structures and used pre-training and data augmentation to effectively improve the detection performance [21]. [PERSON] used the Faster RCNN to identify hyperbolic features of grayscale GPR B-scan images, which could not only judge whether B-scan images contain buried targets but also box out the region where candidate targets are located [22]. However, most of the algorithms for GPR target recognition are based on GPR B-scan images, which cannot fully reflect the characteristics of underground targets. For example, due to the influence of detection direction and image selection methods, the information in the two-dimensional B-scan image may not be recognized.