Study on Significance Enhancement Algorithm of Abnormal Features of Urban Road Ground Penetrating Radar Images

Li, Fanruo; Yang, Feng; Yan, Rui; Qiao, Xu; Xing, Hongjia; Li, Yijin

doi:10.3390/rs14071546

Cited by 12 publications

(3 citation statements)

References 27 publications

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“…In GPR measurements, a wideband signal is often used to capture numerous interfering signals simultaneously, resulting in more effective reflected wave characteristics. However, to extract subgrade defect information using AI models, it is essential to eliminate these interfering signals to enhance the data’s signal-to-noise ratio (SNR) [ 31 ]. Interference signals can come from three aspects [ 32 ]: First, the interference signal from the device system itself includes antennas, cables, and connectors inside.…”

Section: Methodsmentioning

confidence: 99%

Combined CNN and RNN Neural Networks for GPR Detection of Railway Subgrade Diseases

Liu

Wang

Jing

et al. 2023

Sensors

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Vehicle-mounted ground-penetrating radar (GPR) has been used to non-destructively inspect and evaluate railway subgrade conditions. However, existing GPR data processing and interpretation methods mostly rely on time-consuming manual interpretation, and limited studies have applied machine learning methods. GPR data are complex, high-dimensional, and redundant, in particular with non-negligible noises, for which traditional machine learning methods are not effective when applied to GPR data processing and interpretation. To solve this problem, deep learning is more suitable to process large amounts of training data, as well as to perform better data interpretation. In this study, we proposed a novel deep learning method to process GPR data, the CRNN network, which combines convolutional neural networks (CNN) and recurrent neural networks (RNN). The CNN processes raw GPR waveform data from signal channels, and the RNN processes features from multiple channels. The results show that the CRNN network achieves a higher precision at 83.4%, with a recall of 77.3%. Compared to the traditional machine learning method, the CRNN is 5.2 times faster and has a smaller size of 2.6 MB (traditional machine learning method: 104.0 MB). Our research output has demonstrated that the developed deep learning method improves the efficiency and accuracy of railway subgrade condition evaluation.

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Section: Methodsmentioning

confidence: 99%

Combined CNN and RNN Neural Networks for GPR Detection of Railway Subgrade Diseases

Liu

Wang

Jing

et al. 2023

Sensors

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“…Zhao et al [12] incorporated a local plane-wave destruction filter with an online dictionary learning algorithm for individually separation and imaging of GPR diffractions, particularly effective for subsurface small-scale buried targets. Li et al [13] employed wavelet denoising and gamma transformation to suppress the complex background noise and build a multi-scale image brightness and orientation feature pyramid model to enhance abnormal ROI features.…”

Section: Introductionmentioning

confidence: 99%

ROI-Binarized Hyperbolic Region Segmentation and Characterization by Using Deep Residual Convolutional Neural Network with Skip Connection for GPR Imaging

Zhang,

Dai,

Feng

et al. 2024

Applied Sciences

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Ground Penetrating Radar (GPR) is a non-destructive geophysical technique utilizing electromagnetic pulses to detect subsurface material properties. The analysis of regions of interest (ROIs) in GPR images often entails the identification of hyperbolic reflection regions of underground targets through accurate segmentation, a crucial preprocessing step. Currently, this represents a research gap. In the hyperbolic reflection region, manual segmentation not only demands professional expertise but is also time-consuming and error-prone. Automatic segmentation can aid in accurately determining the location and depth of the reflection region, thereby enhancing data interpretation and analysis. This study presents a deep residual Convolutional Neural Network (Res-CNN) that integrates skip connections within an encoder-decoder framework for ROI-binarized hyperbolic segmentation. The proposed framework includes designed downsampling and upsampling modules that facilitate feature computation sharing between these two modules through skip connections within network blocks. In the evaluation of both simple and complex models, our method attained PSNR, SSIM, and FSIM values of 57.1894, 0.9933, and 0.9336, and 58.4759, 0.9958, and 0.9677, respectively. Compared to traditional segmentation methods, the proposed approach demonstrated clearer segmentation results, enabling intelligent and effective identification of the ROI region containing abnormal hyperbolic reflection waves in GPR images.

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“…It has been widely used in urban road [6] maintenance and cavity detection [7]. The GPR transmits electromagnetic waves underground through the transmitter [8]. Meanwhile, the receiver detects the distribution and type of the subsurface media by receiving the short-wave electromagnetic reflection [9] due to the electrical and magnetic inhomogeneity of the medium.…”

Section: Introductionmentioning

confidence: 99%

3D ground penetrating radar cavity identification algorithm for urban roads using transfer learning

Li¹,

Yang²,

Qiao³

et al. 2023

Meas. Sci. Technol.

Self Cite

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3D ground penetrating radar (GPR) is the main method for the detection of underground cavities in urban roads. The number of road cavity samples detected by 3D radar is small, whereas the intelligent identification model require a large number of learning samples for model training, resulting in inadequate model training. This causes the model to be less accurate in identifying cavities, resulting in many misses and misjudgments. Given the above problems, combined with the detection characteristics of the vertical, the horizontal, and the crossed slices obtained in one detection process of 3D GPR, a 3D GPR cavity intelligent recognition model based on model-based transfer learning is proposed. Firstly, a large amount of 3D GPR data of urban road models with cavities are obtained through forwarding simulation. And the intelligent recognition model was pre-trained on the cavity detection data on three types of slices respectively. Then, through model-based transfer learning, a small amount of real underground cavity data is used to speed up the convergence speed of model training and optimize the structural parameters. It breaks through the limitation of the insufficient number of cavity samples for 3D radar detection on the intelligent learning model training, reduces algorithm training costs, and improves identification accuracy.

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Study on Significance Enhancement Algorithm of Abnormal Features of Urban Road Ground Penetrating Radar Images

Cited by 12 publications

References 27 publications

Combined CNN and RNN Neural Networks for GPR Detection of Railway Subgrade Diseases

Combined CNN and RNN Neural Networks for GPR Detection of Railway Subgrade Diseases

ROI-Binarized Hyperbolic Region Segmentation and Characterization by Using Deep Residual Convolutional Neural Network with Skip Connection for GPR Imaging

3D ground penetrating radar cavity identification algorithm for urban roads using transfer learning

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