Automatic Recognition of Category, Burial Depth, and Diameter for Underground Pipeline Using GPR Based on YOLOv12 and Machine Learning Algorithm
Fengbo Ma, Jiachen Gao, Haifeng Pang, Da Yan, Baojun Bai, Yangfan Pei, Shengyuan LiAbstract
Underground pipelines play a vital role in urban operation and development, and the rapid and accurate detection of their distribution remains an urgent challenge. Ground-penetrating radar (GPR) technology has been widely adopted for underground pipeline detection. Therefore, there is currently a need for an automated solution to detect pipeline categories and measure burial depth and diameter using GPR. This study proposes an automatic recognition method of category, burial depth, and diameter for underground pipeline using GPR based on you only look once (YOLO) v12 and a machine learning algorithm. The method operates through three sequential phases. In the first phase, a well-trained YOLOv12 model detects pipeline signal regions and categories in GPR B-scan images, where the model achieves average precision (AP) scores of 0.649 for plastic–water category, 0.654 for electric-cable category, 0.755 for plastic–gas category, and 0.737 for steel–water category, with a mean average precision of 0.699 at intersection over union thresholds from 0.5 to 0.95 with a step size of 0.05. Then, the second phase applies a hyperbolic feature extraction algorithm to derive hyperbolic equations from the detected pipeline signal regions, and the standard deviations of relative errors are 2.9% for vertex vertical coordinate and 12.4% for vertex curvature. The last phase utilizes a well-trained CatBoost regression model with the independent strategy to measure pipeline burial depth and diameter, where the CatBoost model yields mean absolute error of 0.0154 and 0.0185 m, root mean squared error of 0.0261 and 0.0250 m, mean absolute percentage error of 1.38% and 8.41%, and