Development of Adaptive Threshold and Data Smoothening Algorithm for GPR Imaging

Sharma, Prabhat; Kumar, Bambam; Singh, Dharmendra

doi:10.14429/dsj.68.12354

Cited by 4 publications

(3 citation statements)

References 20 publications

(29 reference statements)

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“…The threshold decision is required for filtering of the background noise component and clear identification of the target. For deciding the threshold, find the intersecting point between input data set P (from Eqn (4)) and ANN trained data X ANN by curve fitting and root mean square error method 31 . Further, take the real time pre-processed (windowing, wallparameter subtracting and averaging for subtracting the background) considered target data, which is represented by Xrealtime.…”

Section: Threshold Decisionmentioning

confidence: 99%

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Real time Adaptive Approach for Hidden Targets Shape Identification using through Wall Imaging System

et al. 2021

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In Through-wall Imaging (TWI) system, shape-based identification of the hidden target behind the wall made of any dielectric material like brick, cement, concrete, dry plywood, plastic and Teflon, etc. is one of the most challenging tasks. However, it is very important to understand that the performance of TWI systems is limited by the presence of clutter due to the wall and also transmitted frequency range. Therefore, the quality of obtained image is blurred and very difficult to identify the shape of targets. In the present paper, a shape-based image identification technique with the help of a neural network and curve-fitting approach is proposed to overcome the limitation of existing techniques. A real time experimental analysis of TWI has been carried out using the TWI radar system to collect and process the data, with and without targets. The collected data is trained by a neural network for shape identification of targets behind the wall in any orientation and then threshold by a curve-fitting method for smoothing the background. The neural network has been used to train the noisy data i.e. raw data and noise free data i.e. pre-processed data. The shape of hidden targets is identified by using the curve fitting method with the help of trained neural network data and real time data. The results obtained by the developed technique are promising for target identification at any orientation.

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Section: Threshold Decisionmentioning

confidence: 99%

“…where, p(x) is n th order polynomial, 1 2 1 , , ., n p p p + ……… are its coefficients and x is input to polynomial. The coefficients of polynomial are obtained by the curve fitting approach on the basis of coefficient of determination (R 2 ) values which are greater than 0.9 [31][32] .…”

Section: Threshold Decisionmentioning

confidence: 99%

Real time Adaptive Approach for Hidden Targets Shape Identification using through Wall Imaging System

et al. 2021

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“…Determining an appropriate threshold is crucial for proper imaging in GPR. A higher threshold may exclude important detail, while a lower threshold may include noise, leading to clutter [20]. Several scholars have conducted relevant studies addressing these considerations.…”

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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Effective background removal method based on generative adversary networks

et al. 2020

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Development of Adaptive Threshold and Data Smoothening Algorithm for GPR Imaging

Cited by 4 publications

References 20 publications

Real time Adaptive Approach for Hidden Targets Shape Identification using through Wall Imaging System

Real time Adaptive Approach for Hidden Targets Shape Identification using through Wall Imaging System

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

Effective background removal method based on generative adversary networks

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