Underground Pipeline Precise Positioning Method Based on GPR Bscan Images

Shuangying Sun, Xiaopeng Yang, Tian Lan*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Ground-penetrating radar (GPR) enables the positioning of underground pipelines without excavation. However, the accuracy and errors of existing methods are still insufficient due to the influence of strong clutter in actual environments. In this paper, an underground pipeline precise positioning method based on GPR Bscan images is proposed. First, the YOLOv8 neural network is employed for initially pipeline positioning. Next, by combining network post-processing with curve fitting, the pipeline depth is estimated with high accuracy, enabling precise pipeline positioning. Finally, in validation using measured data, the proposed method achieved a mean average precision (MAP) of 96.8% in the initially pipeline positioning step and a relative depth estimation error of 4.84% in the precise positioning step.

Original languageEnglish
Title of host publicationIEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798331515669
DOIs
Publication statusPublished - 2024
Event2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024 - Zhuhai, China
Duration: 22 Nov 202424 Nov 2024

Publication series

NameIEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

Conference

Conference2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024
Country/TerritoryChina
CityZhuhai
Period22/11/2424/11/24

Keywords

  • Curve fitting
  • Ground-penetrating radar (GPR)
  • Pipeline depth estimation
  • Pipeline positioning
  • YOLO network

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