Robust cascaded frequency filters to recognize rebar in GPR data with complex signal interference

Xiang, Zhongming; Ou, Ge; Rashidi, Abbas

doi:10.1016/j.autcon.2021.103593

Cited by 28 publications

(14 citation statements)

References 29 publications

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“…3) Results on the Open-Source GPR data: The CR-Net is also implemented in open-source GPR radargrams to examine its generalization capability of removing clutter in completely new real-world scenarios. The raw radargrams of scenario 3 (S3) and scenario 4 (S4) are from the rebar dataset [56], and the TU1208 dataset [57], respectively. In S3, the data were collected on a concrete building using the 1500-MHz NJJ-105 GPR.…”

Section: B Experimental Resultsmentioning

confidence: 99%

“…As shown in the raw B-scan of S3 in Fig. 8, the rebar reflection is mixed with a horizontally interfering signal, which challenges the rebar recognition and characterization [56]. In S4, the 200-MHz GSSI system was used to survey a site filled with gneiss as depicted in Fig.…”

Section: B Experimental Resultsmentioning

confidence: 99%

“…After demonstrating the superior clutter removal performance of the CR-Net over the conventional methods, its [56], and the TU1208 dataset [57], respectively. In S3, the data were collected on a concrete building using the handy search NJJ-105 GPR.…”

Section: Comparison Of Clutter Removal Performance Of Different Netwo...mentioning

confidence: 99%

“…Clutter removal performance of different methods on radargrams from open-source datasets. The radargram of scenario 3 (S3) and scenario 4 (S4) are from the rebar dataset[56], and the TU1208 dataset[57], respectively. In S3, the data were collected on a concrete building using the handy search NJJ-105 GPR.…”

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confidence: 99%

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Learning to Remove Clutter in Real-World GPR Images Using Hybrid Data

Sun,

Cheng,

Fan

2022

Preprint

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The clutter in the ground-penetrating radar (GPR) radargram disguises or distorts subsurface target responses, which severely affects the accuracy of target detection and identification. Existing clutter removal methods either leave residual clutter or deform target responses when facing complex and irregular clutter in the real-world radargram. To tackle the challenge of clutter removal in real scenarios, a clutter-removal neural network (CR-Net) trained on a large-scale hybrid dataset is presented in this study. The CR-Net integrates residual dense blocks into the U-Net architecture to enhance its capability in clutter suppression and target reflection restoration. The combination of the mean absolute error (MAE) loss and the multiscale structural similarity (MS-SSIM) loss is used to effectively drive the optimization of the network. To train the proposed CR-Net to remove complex and diverse clutter in real-world radargrams, the first large-scale hybrid dataset named CLT-GPR dataset containing clutter collected by different GPR systems in multiple scenarios is built. The CLT-GPR dataset significantly improves the generalizability of the network to remove clutter in real-world GPR radargrams. Extensive experimental results demonstrate that the CR-Net achieves superior performance over existing methods in removing clutter and restoring target responses in diverse real-world scenarios. Moreover, the CR-Net with its end-to-end design does not require manual parameter tuning, making it highly suitable for automatically producing clutter-free radargrams in GPR applications. The CLT-GPR dataset and the code implemented in the paper can be found at https://haihan-sun.github.io/GPR.html.

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Section: B Experimental Resultsmentioning

confidence: 99%

Section: B Experimental Resultsmentioning

confidence: 99%

Section: Comparison Of Clutter Removal Performance Of Different Netwo...mentioning

confidence: 99%

mentioning

confidence: 99%

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Learning to Remove Clutter in Real-World GPR Images Using Hybrid Data

Sun,

Cheng,

Fan

2022

Preprint

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“…As a result, when detecting hidden infrastructure, the operator should ensure the required accuracy and choose an adequate technology. Since detection results are influenced by various factors, such as the frequency of electromagnetic wave emission [23], the type of soils in which detection works are performed [24], as well as the material from which the hidden objects are made [25][26][27], one should be fully aware of them during the detection process. As a result, operators should use suitable equipment, ensure the repeatability of measurements, and, as far as possible, provide other sources of information that may complement the generated functional model.…”

Section: Introductionmentioning

confidence: 99%

Accuracy Tests and Precision Assessment of Localizing Underground Utilities Using GPR Detection

Karsznia

Onyszko

Borkowska

2021

Sensors

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Applying georadar (GPR) technology for detecting underground utilities is an important element of the comprehensive assessment of the location and ground infrastructure status. These works are usually connected with the conducted investment processes or serialised inventory of underground fittings. The detection of infrastructure is also crucial in implementing the BIM technology, 3D cadastre, and planned network modernization works. GPR detection accuracy depends on the type of equipment used, the selected detection method, and external factors. The multitude of techniques used for localizing underground utilities and constantly growing accuracy demands resulting from the fact that it is often necessary to detect infrastructure under challenging conditions of dense urban development leads to the need to improve the existing technologies. The factor that motivated us to start research on assessing the precision and accuracy of ground penetrating radar detection was the need to ensure the appropriate accuracy, precision, and reliability of detecting underground utilities versus different methods and analyses. The results of the multi-variant GPR were subjected to statistical testing. Various analyses were also conducted, depending on the detection method and on the current soil parameters using a unique sensor probe. When planning detection routes, we took into account regular, established grids and tracked the trajectory of movement of the equipment using GNSS receivers (internal and external ones). Moreover, a specialist probe was used to evaluate the potential influence of the changing soil conditions on the obtained detection results. Our tests were conducted in a developed area for ten months. The results confirmed a strong correlation between the obtained accuracy and the measurement method used, while the correlation with the other factors discussed here was significantly weaker.

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Harmonising Cross-System GPR Wave Amplitude for Concrete Diagnosis with Machine Learning

Wong,

Lai

2023

J Nondestruct Eval

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Robust cascaded frequency filters to recognize rebar in GPR data with complex signal interference

Cited by 28 publications

References 29 publications

Learning to Remove Clutter in Real-World GPR Images Using Hybrid Data

Learning to Remove Clutter in Real-World GPR Images Using Hybrid Data

Accuracy Tests and Precision Assessment of Localizing Underground Utilities Using GPR Detection

Harmonising Cross-System GPR Wave Amplitude for Concrete Diagnosis with Machine Learning

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