Material classification using convolution neural network (CNN) for  x-ray based coded aperture diffraction system

Brumbaugh, Kris; Royse, Camen; Gregory, Christopher; Roe, Kris; Greenberg, Joel A.; Diallo, Souleymane

doi:10.1117/12.2519983

Cited by 6 publications

(8 citation statements)

References 8 publications

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“…Three approaches to classification were compared using different feature sets. The data from the approximately 650 scans was used to train and test a support vector machine (SVM) classifier [7][8][9]. No certified algorithms were used in the calculation of these results.…”

Section: Resultsmentioning

confidence: 99%

Performance of a high-throughput full-tunnel XRD checked baggage scanning system

Coccarelli¹,

Dudley²,

Pike³

et al. 2023

Anomaly Detection and Imaging With X-Rays (ADIX) VIII

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X-ray diffraction imaging (XRDI) offers the potential for reduced false alarm rates, increased throughputs, and more sensitive explosives detection performance in aviation security applications. The deployment of computed tomography (CT) systems across carry-on and checked baggage screening lanes has both reinforced the need for orthogonal detection technologies and created an exciting new opportunity for the implementation of XRDI. Our team at Quadridox built a novel XRDI system that, when combined with a CT system, realizes full-tunnel assessment of checked bags at a belt speed of 20 cm/s. We integrated our XRDI system with a Smiths CTX 5800 explosives detection system (EDS) and collected bag data containing both benign and threat objects. We describe the XRDI system, show examples of the resulting hybrid CT and XRD dataset, and present performance results for the hybrid system.

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Section: Resultsmentioning

confidence: 99%

Performance of a high-throughput full-tunnel XRD checked baggage scanning system

Coccarelli¹,

Dudley²,

Pike³

et al. 2023

Anomaly Detection and Imaging With X-Rays (ADIX) VIII

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“…Brumbaugh et al . used simulated data to train a one-dimensional CNN to identify target materials in XRD-CT datasets [ 170 ]. Relative improvements in classification accuracy were observed compared with standard correlation-based approaches, which were verified on both simulated and real data.…”

Section: Recent Insights From Xds-ctmentioning

confidence: 99%

Recent developments in X-ray diffraction/scattering computed tomography for materials science

Omori,

Bobitan,

Vamvakeros

et al. 2023

Phil. Trans. R. Soc. A.

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X-ray diffraction/scattering computed tomography (XDS-CT) methods are a non-destructive class of chemical imaging techniques that have the capacity to provide reconstructions of sample cross-sections with spatially resolved chemical information. While X-ray diffraction CT (XRD-CT) is the most well-established method, recent advances in instrumentation and data reconstruction have seen greater use of related techniques like small angle X-ray scattering CT and pair distribution function CT. Additionally, the adoption of machine learning techniques for tomographic reconstruction and data analysis are fundamentally disrupting how XDS-CT data is processed. The following narrative review highlights recent developments and applications of XDS-CT with a focus on studies in the last five years. This article is part of the theme issue 'Exploring the length scales, timescales and chemistry of challenging materials (Part 2)'.

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“…The sensing materials and transducers interact with the target analyte, and consequently, their physical properties change, such as dielectric constant, , color, and fluorescence. , These methods are relatively sensitive and accurate; however, the detection of vaporized gases of the target liquid may engender collateral environmental and air pollution. In addition, in industrial or security applications, there are three primary in situ identification schemes: dielectric constant measurement, Raman spectroscopic analysis, and X-ray-based detection . They are generally fast, safe, and user-friendly; nevertheless, these approaches are limited in terms of the bulkiness of the equipment, narrow liquid-species detection range, or high false alarm rate.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, in industrial or security applications, there are three primary in situ identification schemes: dielectric constant measurement, 26 Raman spectroscopic analysis, 27 and X-ray-based detection. 28 They are generally fast, safe, and userfriendly; nevertheless, these approaches are limited in terms of the bulkiness of the equipment, narrow liquid-species detection range, or high false alarm rate. To apply the existing resonatortype all-dielectric metasurface device to the detection of liquid chemicals, the device should be functionalized to ensure its sensitivity to specific species.…”

Section: ■ Introductionmentioning

confidence: 99%

Metasurface-Incorporated Optofluidic Refractive Index Sensing for Identification of Liquid Chemicals through Vision Intelligence

Kim

Choi

et al. 2023

ACS Photonics

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Conventional approaches for the identification of liquid chemicals are bulky and harmful to the environment, detect a limited number of chemical species, produce high false alarm rates, or rely on complex/expensive spectrometers. In this study, a spectrometer-free, accurate metasurface-mediated liquid identification scheme was demonstrated based on optofluidic refractive index (RI) sensing in conjunction with vision intelligence algorithms. A metasurface device integrated into an optofluidic channel provides a polarization-independent focused vortex beam at a single wavelength of 1550 nm, which is highly sensitive to liquid chemicals. The beam patterns respond to the RI and transmission of chemicals, and thus effectively serve as their unique optical "fingerprints". To realize vision intelligence, two deep-learning architectures�a convolutional neural network and a vision transformer�were adopted and trained to classify the beam patterns. A variety of liquid chemicals were successfully identified in situ with over 99% accuracy, requiring no spectrometers. The proposed approach is expected to corroborate the feasibility of artificial intelligence-powered detection schemes that can classify at single wavelengths, unlike conventional instrument-intensive techniques that are attentive to entire spectral responses.

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Material classification using convolution neural network (CNN) for x-ray based coded aperture diffraction system

Cited by 6 publications

References 8 publications

Performance of a high-throughput full-tunnel XRD checked baggage scanning system

Performance of a high-throughput full-tunnel XRD checked baggage scanning system

Recent developments in X-ray diffraction/scattering computed tomography for materials science

Metasurface-Incorporated Optofluidic Refractive Index Sensing for Identification of Liquid Chemicals through Vision Intelligence

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