3-D radar image processing methodology for Non-Destructive Testing of aeronautics composite materials and structures

Brook, Anna; Cristofani, Edison; Vandewal, Marijke; Matheis, Carsten; Jonuscheit, Joachim

doi:10.1109/radar.2012.6212248

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…The last stage is the postprocessing stage, in which the integration between preprocessing and processing methods is tested in order to obtain acceptable results for a practical industrial-oriented NDT technique. 47 The general terminology used in Fig. 4 is presented in Fig.…”

Section: Image Processing Methodologymentioning

confidence: 99%

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Applicability of compressive sensing on three-dimensional terahertz imagery for in-depth object defect detection and recognition using a dedicated semisupervised image processing methodology

Brook

Cristofani

Becquaert

et al. 2013

J. Electron. Imaging

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The quality control of composite multilayered materials and structures using nondestructive tests is of high interest for numerous applications in the aerospace and aeronautics industry. One of the established nondestructive methods uses microwaves to reveal defects inside a three-dimensional (3-D) object. Recently, there has been a tendency to extrapolate this method to higher frequencies (going to the subterahertz spectrum) which could lead to higher resolutions in the obtained 3-D images. Working at higher frequencies reveals challenges to deal with the increased data rate and to efficiently and effectively process and evaluate the obtained 3-D imagery for defect detection and recognition. To deal with these two challenges, we combine compressive sensing (for data rate reduction) with a dedicated image processing methodology for a fast, accurate, and robust quality evaluation of the object under test. We describe in detail the used methodology and evaluate the obtained results using subterahertz data acquired of two calibration samples with a frequency modulated continuous wave system. The applicability of compressive sensing within this context is discussed as well as the quality of the image processing methodology dealing with the reconstructed images

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Section: Image Processing Methodologymentioning

confidence: 99%

“…The preprocessing step is using basic univariate analysis and descriptive statistic for data general image quality 47 and noise level estimation.…”

Section: Image Preprocessingmentioning

confidence: 99%

Applicability of compressive sensing on three-dimensional terahertz imagery for in-depth object defect detection and recognition using a dedicated semisupervised image processing methodology

Brook

Cristofani

Becquaert

et al. 2013

J. Electron. Imaging

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show abstract

“…al. [13] further proposed the use of a portable laboratory device operating in a frequency-modulated continuouswave (FMCW) mode over 40 and 90 GHz bandwidths (centered at 100 and 300 GHz respectively), for the detection of defects in manufactured calibration materials. The recent work by Jamali et.…”

Section: Introductionmentioning

confidence: 99%

Radar-based Materials Classification Using Deep Wavelet Scattering Transform: A Comparison of Centimeter vs. Millimeter Wave Units

Khushaba,

Hill

2022

Preprint

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Radar-based materials detection received significant attention in recent years for its potential inclusion in consumer and industrial applications like object recognition for grasping and manufacturing quality assurance and control. Several radar publications were developed for material classification under controlled settings with specific materials' properties and shapes. Recent literature has challenged the earlier findings on radarsbased materials classification claiming that earlier solutions are not easily scaled to industrial applications due to issues such as the analog-to-digital converters' high sensitivity to target aspect angle, noise fluctuations due to temperature and other external conditions and sensor orientation. Published experiments on the impact of the aforementioned factors on the robustness of the extracted radar-based traditional features have already demonstrated that the application of deep neural networks can mitigate, to some extent, the impact to produce a viable solution. However, previous studies lacked an investigation of the usefulness of lower frequency radar units, specifically < 10GHz, against the higher range units around and above 60GHz. To address the aforementioned investigation, this research considers two radar units with different frequency ranges: the Walabot-3D (6.3-8 GHz) cm-wave and IMAGEVK-74 (62-69 GHz) mm-wave imaging units by Vayyar Imaging. A comparison is presented on the applicability of each unit for material classification. This work also extends upon previous efforts, by applying deep wavelet scattering transform for the identification of different materials based on the reflected signals received by these units. In the wavelet scattering feature extractor, data is propagated through a series of wavelet transforms, nonlinearities, and averaging to produce low-variance representations of the reflected radar signals. This work is unique in terms of the comparison of the utilized radar units and algorithms in material classification and includes real-time demonstrations that show strong performance by both units, with increased robustness offered by the cm-wave radar unit.

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“…As more information can be extracted from digital images, as compared to one dimensional signal, many research areas, including material researches, are now utilizing digital images, such as microscopic images and X-ray images, as one of their evaluation tools. For example, Brook et al [1] used radar images in order to inspect aeronautics composite materials and structures. Works by Kumar, Taheri, and Islam [2] used ultrasonic images in order to evaluate and detects several defects in a material.…”

Section: Introductionmentioning

confidence: 99%

Variations on Impulse Noise Model in Digital Image Processing Field: A Survey on Current Research Inclination

Teoh¹

2013

IJIMT

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As the information from digital images are easier to be evaluated as compared with one dimensional signals, digital images are now commonly used in may research fields. Unfortunately, similar to other digital signals, digital images are also sometimes unintentionally corrupted by unwanted signals, called noise. One of the noises commonly corrupting digital image is the impulse noise. Therefore, impulse noise reduction has become one of the active researches in these recent years. Many impulse noise models have been proposed by researchers for this research purpose. Therefore, the purpose of this paper is to investigate the popularity of these noise models. The research is done by a survey on available online materials. However, because there are more than thousands of related articles available online, the survey was carried out based on the keywords related to those articles. The research was restricted only to IEEExplore® database. The result from this survey shown that the research related to impulse noise model in digital image processing is still showing an increasing trend. Among the impulse noise models, the saltand-pepper noise is the most used impulse noise model in current literature.

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3-D radar image processing methodology for Non-Destructive Testing of aeronautics composite materials and structures

Cited by 5 publications

References 17 publications

Applicability of compressive sensing on three-dimensional terahertz imagery for in-depth object defect detection and recognition using a dedicated semisupervised image processing methodology

Applicability of compressive sensing on three-dimensional terahertz imagery for in-depth object defect detection and recognition using a dedicated semisupervised image processing methodology

Radar-based Materials Classification Using Deep Wavelet Scattering Transform: A Comparison of Centimeter vs. Millimeter Wave Units

Variations on Impulse Noise Model in Digital Image Processing Field: A Survey on Current Research Inclination

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