GPR-based Model Reconstruction System for Underground Utilities Using GPRNet

Feng, Jinglun; Yang, Liang; Hoxha, Ejup; Sanakov, Diar; Sotnikov, Stanislav; Xiao, Jizhong

doi:10.1109/icra48506.2021.9561355

Cited by 10 publications

(3 citation statements)

References 23 publications

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“…During the last decade, most of the efforts in GPR developments within this application field include new processing techniques and methodologies for 3D reconstruction and visualization, machine learning techniques for automatic detection (mainly objects with a circular section such as bars and pipes, but also cracks), and data digitization into GIS models [60][61][62][63][64][65][66][67]. Moreover, new multi-antenna systems and array multi-channel antennas have been developed, thus allowing for a dense 3D data collection [68], thus reducing the surveying time and increasing the productivity.…”

Section: Gpr Applications In Civil Engineeringmentioning

confidence: 99%

From Its Core to the Niche: Insights from GPR Applications

2022

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Thanks to its non-destructive, high-resolution imaging possibilities and its sensitivity to both conductive and dielectric subsurface structures, Ground-Penetrating Radar (GPR) has become a widely recognized near-surface geophysical tool, routinely adopted in a wide variety of disciplines. Since its first development almost 100 years ago, the domain in which the methodology has been successfully deployed has significantly expanded from ice sounding and environmental studies to precision agriculture and infrastructure monitoring. While such expansion has been clearly supported by the evolution of technology and electronics, the operating principles have always secured GPR a predominant position among alternative inspection approaches. The aim of this contribution is to provide a large-scale survey of the current areas where GPR has emerged as a valuable prospection methodology, highlighting the reasons for such prominence and, at the same time, to suggest where and how it could be enhanced even more.

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Section: Gpr Applications In Civil Engineeringmentioning

confidence: 99%

From Its Core to the Niche: Insights from GPR Applications

2022

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“…In terms of pipeline inspection, Luo and Lai [25] built a cavity pattern database containing C-scan and B-scan, where C-scan provides object location information and B-scan helps to recognize the object type. Feng et al [26] presented a deep neural network (DNN) migration module to interpret the raw GPR B-scan image into a cross-section of the target model and generated a pipe model using a DNN-based 3D reconstruction module.…”

Section: Introductionmentioning

confidence: 99%

3D imaging and temporal evolution recognition of concrete internal defects based on GPR

Wang,

Li,

Lei

et al. 2024

Meas. Sci. Technol.

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Periodic inspections and recognition of temporal evolution for concrete internal defects are important for the long-term operation of civil engineering infrastructures. This study proposes a method integrating convolutional neural network (CNN) with maximum intensity projection (MIP) for 3D imaging and temporal evolution recognition of concrete internal defects. The proposed method utilizes MIP to process multiple ground penetrating radar (GPR) B-scans and generates 2D projected GPR B-scans containing spatial information. SegNet coupled with the Lovász softmax loss function is introduced to reconstruct 2D defects from projected GPR B-scans. Subsequently, 3D imaging result is reconstructed from 2D imaging results by 3D reconstruction module. Finally, the defect change extraction module realizes recognition of defect changes based on 3D imaging results at different time. The superiority of proposed method is validated based on both synthetic and real GPR data, which presents better recognition results and less time consumption than existing 3D CNN-based method.

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“…Beneficially, C-scan datasets can exploit time-slicing [104][105][106] (Figure 6a,b), in situ transparency filtering [107] and false-colouration [108] to improve conveyance of 3D forms. More recent investigations into true-3D volumetric reconstruction [98,[109][110][111][112][113][114][115][116][117] (Figure 6c) show promise for advances towards practically viable fully immersive GPR-based subsurface inspection surveys (undertaken in fully digitised virtual survey environments) [80,[118][119][120][121][122]. Whilst conceivable and under trial, achieving mainstream commercial deployment will require blind spot alleviation through adoption of rotary scan motion complementary to tunnel curvature, possibly similar to the superposition of concentric cylindrical 'look-ahead' radargrams pioneered by the TULIPS system [123] for tunnel excavation monitoring.…”

mentioning

confidence: 99%

Developments in 3D Visualisation of the Rail Tunnel Subsurface for Inspection and Monitoring

2022

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Railway Tunnel SubSurface Inspection (RTSSI) is essential for targeted structural maintenance. ‘Effective’ detection, localisation and characterisation of fully concealed features (i.e., assets, defects) is the primary challenge faced by RTSSI engineers, particularly in historic masonry tunnels. Clear conveyance and communication of gathered information to end-users poses the less frequently considered secondary challenge. The purpose of this review is to establish the current state of the art in RTSSI data acquisition and information conveyance schemes, in turn formalising exactly what constitutes an ‘effective’ RTSSI visualisation framework. From this knowledge gaps, trends in leading RTSSI research and opportunities for future development are explored. Literary analysis of over 300 resources (identified using the 360-degree search method) informs data acquisition system operation principles, common strengths and limitations, alongside leading studies and commercial tools. Similar rigor is adopted to appraise leading information conveyance schemes. This provides a comprehensive whilst critical review of present research and future development opportunities within the field. This review highlights common shortcomings shared by multiple methods for RTSSI, which are used to formulate robust criteria for a contextually ‘effective’ visualisation framework. Although no current process is deemed fully effective; a feasible hybridised framework capable of meeting all stipulated criteria is proposed based on identified future research avenues. Scope for novel analysis of helical point cloud subsurface datasets obtained by a new rotating ground penetrating radar antenna is of notable interest.

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GPR-based Model Reconstruction System for Underground Utilities Using GPRNet

Cited by 10 publications

References 23 publications

From Its Core to the Niche: Insights from GPR Applications

From Its Core to the Niche: Insights from GPR Applications

3D imaging and temporal evolution recognition of concrete internal defects based on GPR

Developments in 3D Visualisation of the Rail Tunnel Subsurface for Inspection and Monitoring

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