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Non-metallic pipe detection using SF-GPR: A new approach using neural network | IEEE Conference Publication | IEEE Xplore

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


Abstract:

Currently, mean subtraction, median removal, singular value decomposition (SVD), Principal component analysis (PCA) and Independent component analysis (ICA) areverypopula...Show More

Abstract:

Currently, mean subtraction, median removal, singular value decomposition (SVD), Principal component analysis (PCA) and Independent component analysis (ICA) areverypopular approaches to extract the buried target information in presence of clutter and background noise for GPR applications. Clutter and background reduction and detection of low dielectric constant buried object with variable soil conditionsare the challenging tasks in GPR. But available techniques are not able to extract the non-metallic target information, due to low dielectric constant. Therefore, this paper proposes a neural network and statistical mean to standard deviation threshold based approach for subtracting background and for enhancing the detection of low dielectric constant buried object. ANN approach is based on the collection of large amount background data with soil moisture variation. These background data statistically analysed to compute the mean to standard deviation thresholding. After that, motion filter estimate the actual pixel intensity of PVC pipe in linear manner. The results show that the enhanced target detection and background subtraction are achieved directly from proposed trained neural network.
Date of Conference: 10-15 July 2016
Date Added to IEEE Xplore: 03 November 2016
ISBN Information:
Electronic ISSN: 2153-7003
Conference Location: Beijing, China

References

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