## Abstract
As shown in Figure 1, when the surveying line direction of GPR is perpendicular to the direction of the pipeline, it is called a longitudinal pipeline (when the direction of the GPR surveying line is at an angle greater than 45\({}^{\circ}\) to the direction of the pipe, it is called a longitudinal line), and its B-Scan image shows a hyperbolic shape. When the surveying line direction of GPR is the same as that of the pipeline, it is called a transverse pipeline (when the direction of the GPR surveying line is at an angle less than 45\({}^{\circ}\) to the direction of the pipe, it is called a transverse line), and the B-Scan image of GPR does not show a hyperbolic shape but rather a straight-line shape. In addition, both underground pipelines and cavities show hyperbolic features on B-Scan images, which are difficult to distinguish, as shown in Figure 2. Therefore, to overcome the limitations of B-Scan images, it is necessary to use 3-dimensional GPR data, which can better reflect the underground structures to classify objects and obtain reliable classification results. Deep learning has dominated the field of 2D computer vision, and its application to more complex problems, such as 3D data, is a hot trend. In recent years, researchers have made many attempts to identify 3D objects (such as RGB-D images, CAD models, 3D point cloud images, etc.). There are roughly two main directions for 3D image recognition. Figure 1: GPR images of longitudinal and transverse pipelines. (**a**) B-scan images of the longitudinal pipeline. (**b**) B-scan image of the transverse pipeline. (**c**) 3D GPR image and left view of the lateral pipeline. Figure 2: GPR image cross-sectional analysis of the pipeline and the cavity. (**a**) GPR image of the pipeline. (**b**) GPR image of the cavity. One is to transform 3D to 2D by projection, multi-angle observation, image Mosaic, and other methods, in which the latest 2D target recognition method can be applied [23; 24; 25]. In 2019, [PERSON] et al. [26] proposed a method for detecting underground caverns using 3-dimensional GPR data based on deep convolutional neural networks. They took advantage of a novel 2D grid image, which consists of several horizontal and longitudinal images from 3D data and conducted experimental verification by training a convolutional neural network and using real 3-dimensional GPR data obtained from urban roads in Seoul, Korea. Later, a deep learning model called UcNet was proposed in the paper [27]. Based on classification and recognition by CNN (Convolutional Neural Network) in the paper [26], UcNet selects images with detected cavities for pixel augmentation and further screening by phase analysis. The results showed that compared with traditional CNN, the misclassification of the underground cavity is significantly reduced. However, the research above did not directly deal with 3D data and instead only extracted multiple 2D images from 3D images to reflect their spatial characteristics. As a result, 3-dimensional GPR data could not be fully utilized, causing unsatisfactory classification results. Additionally, the method of 2D data input largely depends on the selection method that can represent the 2D data correlation, which is unstable. The other is to directly take 3D data as input and directly use a 3D voxel input [28; 29]. In 2020, [PERSON] et al. proposed a method based on a 3D-CNN to directly use 3D data to classify underground targets [30]. The average accuracy of the four types of underground targets verified by real-world data was 97%. The 3D voxel image is taken as the input of the deep learning model, which can completely retain the spatial characteristics of 3D data. However, the subsequent problem is that the training of 3D-CNN is very expensive in terms of calculation, and its model size is also larger, so it is not suitable for final deployment to platforms with limited computing resources.