Rapid GPU-based simulation of x-ray transmission, scatter, and phase measurements for threat detection systems

Gong, Qian; Coccarelli, David; Stoian, Razvan-Ionut; Greenberg, Joel A.; Vera, Esteban; Gehm, Michael E.

doi:10.1117/12.2223244

Cited by 5 publications

(5 citation statements)

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“…For example, it is difficult to generate out-of-plane rotations, so augmentation is usually limited to in-plane rotations of the staged threat items. Potential remedies include either developing a framework for collecting the optimal number of threat poses to make accurate interpolation of intermediate out-of-plane rotations, or generating realistic threat radiography from realistic 3D CAD models of threats [83,30]. The requirements for a solution are that out-of-plane rotations are accurate, realistic, and can be computed efficiently or on-the-fly.…”

Section: Discussion On Image Preprocessingmentioning

confidence: 99%

Automated X-ray image analysis for cargo security: Critical review and future promise

Rogers

Jaccard

Morton³

et al. 2017

XST

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We review the relatively immature field of automated image analysis for X-ray cargo imagery. There is increasing demand for automated analysis methods that can assist in the inspection and selection of containers, due to the ever-growing volumes of traded cargo and the increasing concerns that customs-and securityrelated threats are being smuggled across borders by organised crime and terrorist networks. We split the field into the classical pipeline of image preprocessing and image understanding. Preprocessing includes: image manipulation; quality improvement; Threat Image Projection (TIP); and material discrimination and segmentation. Image understanding includes: Automated Threat Detection (ATD); and Automated Contents Verification (ACV). We identify several gaps in the literature that need to be addressed and propose ideas for future research. Where the current literature is sparse we borrow from the single-view, multi-view, and CT X-ray baggage domains, which have some characteristics in common with X-ray cargo.

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Section: Discussion On Image Preprocessingmentioning

confidence: 99%

Automated X-ray image analysis for cargo security: Critical review and future promise

Rogers

Jaccard

Morton³

et al. 2017

XST

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“…We will also investigate using 3D ComputerAided Design (CAD) models [24], or Computed Tomography (CT) scans, of threats as a basis for generate realistic synthetic radiographs which can be used in TIP, and whether they can improve the performance of ML-based algorithms. This could also solve the problem of generating out-of-plane threat rotations and improve the speed with which new threats can be added to the detection capabilities of an ATD system.…”

Section: Discussionmentioning

confidence: 99%

Threat Image Projection (TIP) into X-ray images of cargo containers for training humans and machines

Rogers

Jaccard

Protonotarios

et al. 2016

2016 IEEE International Carnahan Conference on Security Technology (ICCST)

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Abstract-We propose a framework for Threat Image Projection (TIP) in cargo transmission X-ray imagery. The method exploits the approximately multiplicative nature of X-ray imagery to extract a library of threat items. These items can then be projected into real cargo. We show using experimental data that there is no significant qualitative or quantitative difference between real threat images and TIP images. We also describe methods for adding realistic variation to TIP images in order to robustify Machine Learning (ML) based algorithms trained on TIP. These variations are derived from cargo X-ray image formation, and include: (i) translations; (ii) magnification; (iii) rotations; (iv) noise; (v) illumination; (vi) volume and density; and (vii) obscuration. These methods are particularly relevant for representation learning, since it allows the system to learn features that are invariant to these variations. The framework also allows efficient addition of new or emerging threats to a detection system, which is important if time is critical.We have applied the framework to training ML-based cargo algorithms for (i) detection of loads (empty verification), (ii) detection of concealed cars (ii) detection of Small Metallic Threats (SMTs). TIP also enables algorithm testing under controlled conditions, allowing one to gain a deeper understanding of performance. Whilst we have focused on robustifying ML-based threat detectors, our TIP method can also be used to train and robustify human threat detectors as is done in cabin baggage screening.

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“…The NVIDIA OptiX ray tracing engine has been recently used in different tools for several applications such as particle-based simulations [ 10 ], threat detection systems [ 11 ], PET scans Monte Carlo simulations [ 12 ], X-ray transmission imaging [ 13 ], and electromagnetic characterization [ 14 , 15 ]. However, although previous works [ 14 , 15 , 16 , 17 ] have introduced the idea of using the OptiX framework to accelerate the ray tracing, the authors have not found any work in the literature that focuses on the calculation of the received power in broad areas using a measurement plane as a receiver.…”

Section: Introductionmentioning

confidence: 99%

Accelerated Ray Launching Method for Efficient Field Coverage Studies in Wide Urban Areas

Gómez,

Tayebi,

Hellín

et al. 2023

Sensors

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The implementation of a fast and efficient computer tool for field coverage studies in urban mobile radio systems is presented in this work. An accelerated and tailored ray launching method takes advantage of a ray tracing programmable framework optimized for massively parallel processing on GPUs. The PlotOptiX API is used to customize the code before applying the electromagnetic equations. The proposed code is described, and results are shown to demonstrate its correct operation. A high number of diffractions and reflections can be tracked in each ray from the transmitter to the receiver. In addition to the typical point-to-point simulation, measurement planes can also be set as receivers to provide fast predictions in wide urban areas.

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Rapid GPU-based simulation of x-ray transmission, scatter, and phase measurements for threat detection systems

Cited by 5 publications

References 10 publications

Automated X-ray image analysis for cargo security: Critical review and future promise

Automated X-ray image analysis for cargo security: Critical review and future promise

Threat Image Projection (TIP) into X-ray images of cargo containers for training humans and machines

Accelerated Ray Launching Method for Efficient Field Coverage Studies in Wide Urban Areas

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