## Abstract  
Ground penetrating radar (GPR), as a non-destructive and rapid detection instrument, has been widely used for underground pipeline detection. However, as the interpretation of 3-dimensional GPR images is still manually performed, the process is inefficient. Aiming at solving the challenges of automatic recognition for underground pipelines, we propose a recognition method based on a deep learning algorithm, which uses 3-dimensional GPR images and the improved 3D depth-wise separable convolution block. In order to expand the number of samples in the dataset, we propose a data augmentation method based on three-dimensional matrix rotation and use a wavelet-based denoising method to filter out the direct wave interference. To prove the effectiveness and efficiency of our method, we compared the classification performance of the improved 3D depth-wise separable convolutional block with the traditional 3D convolutional block and the ordinary 3D depth-wise separable convolutional block under the same conditions. According to the experiment's results, the number of parameters of the method we proposed is 66.9% less than that of the traditional 3D convolution method, while the classification performance is similar. Furthermore, compared with ordinary 3D depth-wise separable convolution, our method can significantly improve the classification and recognition ability of the neural network, while the number of calculations and the number of parameters remain almost the same. This study demonstrates the effectiveness of 3D-CNN in the field of GPR image interpretation. An improved 3D depth-wise separable convolutional block is also proposed. It greatly reduces the amount of calculation and parameters while ensuring classification performance. It is better than the existing algorithms in performance. At the same time, to obtain the position and direction of the pipeline, in this study, a conic fitting method using the Canny operator is proposed to locate the vertices of B-Scan images and record their horizontal and vertical coordinates. This method can estimate the direction of the pipeline and it lays the foundation for future work such as measuring the pipeline depth. They cause serious property damage and casualties [1]. To prevent such accidents, it is imperative to establish a complete monitoring and evaluation mechanism that can strengthen the supervision of the maintenance of underground facilities [2]. Compared with traditional detection methods, GPR technology has the advantages of non-destructive detection, no manual excavation is needed, and no damage to the ground occurs. Therefore, not only is GPR very flexible and convenient to deploy but is also very effective in underground detection. In addition, the detection accuracy of GPR is high, which can precisely distinguish the difference between underground objects and the medium and accurately detect the location and shape of the target. Because of its good performance, GPR is widely used to detect shallow underground objects in various fields, such as bridge deck detection, archaeology, and the detection of buried explosives [3; 4; 5]. Therefore, the application of GPR in underground pipeline detection is a direction with development prospects and practical application value. However, due to the complex geometry of underground objects, the variation of water content in the underground medium, and the interference of other underground objects, the interpretation of GPR data is a very challenging issue. It is usually difficult to obtain reliable results by directly analyzing the original GPR images, so underground objects are usually identified by detecting hyperbola in GPR B-scan images [6]. However, it is still very difficult to interpret GPR data with human vision. An expert may only be able to analyze GPR data for a few kilometers of urban roads in a week, and due to various subjective reasons, these interpretations may not be reliable.