volume | PIER Journals

Abstract

As a popular nondestructive technique, ground penetrating radar (GPR) is extensively utilized for detecting underground pipelines. In this paper, an efficient and automatic scheme is presented for the detection and classification of underground pipelines by combining electromagnetic modeling and machine learning techniques. By virtue of open-source gprMax software, the B-Scan signatures of underground pipelines are simulated and analyzed in detail, with four types of underground pipelines taken into account, i.e., iron pipelines, concrete pipelines, copper pipelines, and PVC pipelines. On the basis of electromagnetic modeling, B-scan profiles of underground pipelines are preprocessed by using the average method and time gain compensation method to obtain a dataset for training neural network of YOLOv8 model. The simulations indicate that our scheme combining simulated B-Scan profiles and YOLOv8 model is able to detect and classify underground pipelines with high accuracy, and the category and material of underground pipelines can be determined with a high confidence level. Specifically, the detection time of a single B-scan image for underground pipelines is about 0.02s, and the average detection accuracy can reach 0.995, which is potentially valuable for the automatic detection and classification of underground pipelines in GPR applications.

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Dr. Qiang Guo

Dr. Peng-Ju Yang

Dr. Rui Wu

Dr. Yuqiang Zhang