Non-metallic pipe detection using SF-GPR: A new approach using neural network

Sharma, Prabhat; Kumar, Bambam; Singh, Dharmendra; Gaba, S.P.

doi:10.1109/igarss.2016.7730726

Cited by 11 publications

(9 citation statements)

References 3 publications

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“…Arti cial neural networks are common use to solve the problems of automated classi cation and location recognition of underground objects [17][18][19][20].…”

Section: Related Workmentioning

confidence: 99%

“…The proposed mine detection and recognition methods with UWB radars and arti cial neural networks [10][11][12][13][14][15][16][17][18][19][20] require further development of the landmine localization methodology for the demining task. The reason for that is a problem with the accuracy of determining the location of underground objects and registration of part of the re ected signal, which carries a piece of information about the underground object, that occurs when creating automated mine detection and recognition systems.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Small military and industrial under-surface objects detection using data from the UWB GPR, MLP filter, and oscillatory neural network

Peleshchak

Lytvyn

Mediakov

et al. 2023

Preprint

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In the paper, we present a solution to the problem of recognizing subsurface objects detected by UWB radar using a hybrid neural network consisting of an MLP filter, a Hilbert block, and an oscillatory neural network. Based on information resonance, this hybrid neural network architecture recognizes low-amplitude noisy signals that are signs of subsurface objects. In addition, the proposed neural network model can filter reflected signals with interference and amplify valuable signals due to resonance and has higher performance than existing models of artificial neural networks.

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“…Arti cial neural networks are common use to solve the problems of automated classi cation and location recognition of underground objects [17][18][19][20].…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Small military and industrial under-surface objects detection using data from the UWB GPR, MLP filter, and oscillatory neural network

Peleshchak

Lytvyn

Mediakov

et al. 2023

Preprint

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“…In addition to the above references, the application of neural networks to analyze GPR data has been recently considered. The use of SF-GPRs, neural networks for non-metallic pipe detection [16], pipe crack detection algorithm [17], identification of concealed targets inside the book [18], and object location classification [19] are some examples in this regard. Dumin et al provided a brief review in this regard [20].…”

Section: Introductionmentioning

confidence: 99%

GPR Data Regression and Clustering by the Fuzzy Support Vector Machine and Regression

Hosseinzadeh¹,

Shaghaghi²

2020

PIER M

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In this paper, the problem of determining the depth and radius of a circular pipe along with the soil characteristics is studied, using electromagnetic waves with a fuzzy support vector machine as well as a fuzzy support vector machine. To this end, three neural network based fuzzy support vectors are used to determine the soil, depth, and dimensions. Also, using the 2D time domain numerical simulations of electromagnetic field scattering, along with MATLAB software, 1030 data are generated for training as well as neural network verification. Given the fact that for each of the three parameters the nature of the problem is different, separate neural networks are considered with different parameters, thus the number of different data for the network training is considered. In all three cases, the neural network parameters are optimized using genetic algorithm to reduce the error and also reduce the number of support vectors. It should be noted that the objective function of the genetic algorithm consists of two components of the error, as well as the number of membership functions, which can be determined by determining a control parameter. For soil permittivity, the algorithm can accurately predict 93% of permittivities, and it decreases to 89.8 for the pipe depth determination. For diameter it is seen that for 69.3 of the cases the algorithm can correctly classify the pipes.

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“…is the relative dielectric constant of medium = When dealing with GPR images, there are several different types of 'scans'. The simple GPR range profile is known as A-scan24 , which is as shown inFig. 2.…”

mentioning

confidence: 99%

Development of Adaptive Threshold and Data Smoothening Algorithm for GPR Imaging

2018

Self Cite

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There are many approaches available to separate the background and foreground in image processing applications. Currently, researchers are focusing on wavelet De-noising, curvelet threshold, Edge Histogram Descriptor threshold, Otsu thresholding, recursive thresholding and adaptive progressive thresholding. In fixed and predictable background conditions, above techniques separate background and foreground efficiently. In a common scenario, background reference is blind due to soil surface moisture content and its non-linearity. There are many methodologies proposed from time to time by researchers to solve this blind reference background separation. But challenges still now remain, because there are two major problems in ground penetrating radar imaging such as targets like ground enhances the false alarm and non-metallic target detection, where the threshold decision is a critical task. In this paper, a novel real time blind adaptive threshold algorithm is proposed for ground penetrating radar image processing. The blind threshold was decided to use normal random variable variance and image data variance. Further, the image was smoothened by random variance ratio to image data variance. Experimental results showed satisfactory results for the background separation and smoothening the targeted image data with the proposed algorithm.

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Non-metallic pipe detection using SF-GPR: A new approach using neural network

Cited by 11 publications

References 3 publications

Small military and industrial under-surface objects detection using data from the UWB GPR, MLP filter, and oscillatory neural network

Small military and industrial under-surface objects detection using data from the UWB GPR, MLP filter, and oscillatory neural network

GPR Data Regression and Clustering by the Fuzzy Support Vector Machine and Regression

Development of Adaptive Threshold and Data Smoothening Algorithm for GPR Imaging

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