## 2 Deep Learning Based Underground Pipeline Target Recognition Using 3-Dimensional GPR Data
### Determination of Pipeline Position and Direction  
In practical applications, it is often necessary not only to know whether there is a pipeline target in the GPR image but also to know the specific position and direction of the pipeline. Therefore, we propose a pipeline-positioning scheme.  
In the paper, the pipeline target is divided into two types: A transverse pipeline and a longitudinal pipeline. Three-dimensional GPR data can be regarded as the combination of B-Scans of multiple channels. Therefore, during pipeline positioning, a B-Scan can be used for positioning, and the pipeline positioning can be realized by extracting the vertices of the hyperbola, and then the position and direction of the pipeline in 3D space can be  
Figure 19: Improved depth-wise separable convolution block structure diagram.  
Figure 20: Convolutional neural network architecture for 3D GPR image classification.  
obtained by connecting the positions of vertices in multiple B-Scans. It should be noted that for the horizontal pipeline because the hyperbola is on the 2D B-Scan in the direction of the GPR array, it needs to be rotated before processing, as shown in Figure 21. The red line in Figure 21 shows the line at the apex of the hyperbola.  
The extraction of hyperbolic vertices in B-Scan images is the most important step to determine the pipeline location and direction in 3D space. Therefore, we propose a pipeline location scheme based on the Canny operator conic curve fitting method. Figure 22 shows the processing of the simulated B-Scan, and the specific steps corresponding to the numbers in the figure are as follows:  
1. The B-Scan image is processed by median filtering to remove the impulse noise and protect the signal edge from being blurred.
2. The canny operator is used for edge detection. The canny operator is a very effective and adaptable algorithm in the field of edge detection. It can adjust parameters to identify different edges according to different application requirements and has the advantages of a low error rate and accurate edge positioning. A Gaussian filter is first used for smoothing and filtering. Then the gradient and gradient direction is found, and non-maximal value suppression is used to refine the edges. Finally, isolated low-threshold points are suppressed, and edges are connected using double-threshold detection and connectivity detection.
3. Trade-offs are made based on edge cases. In the case of multiple edge points in the same column, coordinate averaging is used to calculate the average. In addition, the edge points obtained by the Canny operator may be discontinuous, so the points whose height difference exceeds the threshold are discarded and replaced by a row of edge points.
4. Regarding curve fitting, each pixel is scanned line by line, and we record the coordinates and number of white points (value 1) and use the quadratic function to perform curve-fitting on the edge points after processing.
5. For vertex determination, the derivative of the fitted quadratic curve is obtained, and the points where the derivative is 0 are marked and the horizontal and vertical coordinates are recorded.  
Figure 21: Three-dimensional space pipeline-positioning diagram.