Lightweight framework for underground pipeline recognition and spatial localization based on multi-view 2D GPR images

TL;DR

This paper presents a novel 3D pipeline detection framework using multi-view 2D GPR images, combining advanced feature evaluation, a modified YOLO algorithm, and spatial matching to improve accuracy and robustness in complex underground scenarios.

Contribution

It introduces a 3D pipeline detection framework integrating multi-view analysis, a modified YOLOv11 with cross-dimensional mechanisms, and a 3D spatial matching algorithm, enhancing small-scale feature recognition and multi-view data association.

Findings

Achieved 96.2% accuracy in complex scenarios

Improved detection recall to 93.3%

Enhanced small-scale feature extraction capabilities

Abstract

To address the issues of weak correlation between multi-view features, low recognition accuracy of small-scale targets, and insufficient robustness in complex scenarios in underground pipeline detection using 3D GPR, this paper proposes a 3D pipeline intelligent detection framework. First, based on a B/C/D-Scan three-view joint analysis strategy, a three-dimensional pipeline three-view feature evaluation method is established by cross-validating forward simulation results obtained using FDTD methods with actual measurement data. Second, the DCO-YOLO framework is proposed, which integrates DySample, CGLU, and OutlookAttention cross-dimensional correlation mechanisms into the original YOLOv11 algorithm, significantly improving the small-scale pipeline edge feature extraction capability. Furthermore, a 3D-DIoU spatial feature matching algorithm is proposed, which integrates three-dimensional geometric constraints and center distance penalty terms to achieve automated association of multi-view annotations. The three-view fusion strategy resolves inherent ambiguities in single-view detection. Experiments based on real urban underground pipeline data show that the proposed method achieves accuracy, recall, and mean average precision of 96.2%, 93.3%, and 96.7%, respectively, in complex multi-pipeline scenarios, which are 2.0%, 2.1%, and 0.9% higher than the baseline model. Ablation experiments validated the synergistic optimization effect of the dynamic feature enhancement module and Grad-CAM++ heatmap visualization demonstrated that the improved model significantly enhanced its ability to focus on pipeline geometric features. This study integrates deep learning optimization strategies with the physical characteristics of 3D GPR, offering an efficient and reliable novel technical framework for the intelligent recognition and localization of underground pipelines.