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

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

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

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 security-related 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

A Review of Automated Image Understanding within 3D Baggage Computed Tomography Security Screening

Baggage inspection is the principal safeguard against the transportation of prohibited and potentially dangerous materials at airport security checkpoints. Although traditionally performed by 2D X-ray based scanning, increasingly stringent security regulations have led to a growing demand for more advanced imaging technologies. The role of X-ray Computed Tomography is thus rapidly expanding beyond the traditional materials-based detection of explosives. The development of computer vision and image processing techniques for the automated understanding of 3D baggage-CT imagery is however, complicated by poor image resolutions, image clutter and high levels of noise and artefacts. We discuss the recent and most pertinent advancements and identify topics for future research within the challenging domain of automated image understanding for baggage security screening CT

Efficient Bandwidth Estimation in 2D Filtered Backprojection Reconstruction

A generalized cross-validation approach to estimate the reconstruction filter bandwidth in 2D filtered backprojection is presented. The method writes the reconstruction equation in equivalent backprojected filtering form, derives results on eigendecomposition of symmetric 2D circulant matrices, and applies them to make bandwidth estimation a computationally efficient operation within the context of standard backprojected filtering reconstruction. Performance evaluations on a range of simulated emission tomography experiments give promising results. The superior performance holds at both low and high total expected counts, pointing to the method\u27s applicability even in weak signal-to-noise-ratio situations. The approach also applies to the more general class of elliptically symmetric filters, with the reconstructed estimate\u27s performance often better than even that obtained with the true optimal radially symmetric filter

Characterization of Porosity Defects in Selectively Laser Melted IN718 and Ti- 6A1-4V via Synchrotron X-Ray Computed Tomography

Additive manufacturing (AM) is a method of fabrication involving the joining of feedstock material together to form a structure. Additive manufacturing has been developed for use with polymers, ceramics, composites, biomaterials, and metals. Of the metal additive manufacturing techniques, one of the most commonly employed for commercial and government applications is selective laser melting (SLM). SLM operates by using a high-powered laser to melt feedstock metal powder, layer by layer, until the desired near-net shape is completed. Due to the inherent function of AM and particularly SLM, it holds much promise in the ability to design parts without geometrical constraint, cost-effectively manufacture them, and reduce material waste. Because of this, SLM has gained traction in the aerospace, automotive, and medical device industries, which often use uniquely shaped parts for specific functions. These industries also have a tendency to use high performance metallic alloys that can withstand the sometimes-extreme operating conditions that the parts experience. Two alloys that are often used in these parts are Inconel 718 (IN718) and Ti-6Al-4V (Ti64). Both of these materials have been routinely used in SLM processing but have been often marked by porosity defects in the as-built state. Since large amounts of porosity is known to limit material mechanical performance, especially in fatigue life, there is a general need to inspect and quantify this material characteristic before part use in these industries. One of the most advanced porosity inspection methods is via X-ray computed tomography (CT). CT uses a detector to capture scattered X-rays after passing through the part. The detector images are then reconstructed to create a tomograph that can be analyzed using image processing techniques to visualize and quantify porosity. In this research, CT was performed on both materials at a 30 μm “low resolution” (LR) for different build orientations and processing conditions. Furthermore, a synchrotron beamline was used to conduct CT on small samples of the SLM IN718 and Ti64 specimens at a 0.65 μm “high resolution” (HR), which to the author’s knowledge is the highest resolution (for SLM IN718) and matches the highest resolution (for SLM Ti64) reported for porosity CT investigations of these materials. Tomographs were reconstructed using TomoPy 1.0.0, processed using ImageJ and Avizo 9.0.2, and quantified in Avizo and Matlab. Results showed a relatively low amount of porosity in the materials overall, but a several order of magnitude increase in quantifiable porosity volume fraction from LR to HR observations. Furthermore, quantifications and visualizations showed a propensity for more and larger pores to be present near the free surfaces of the specimens. Additionally, a plurality of pores in the HR samples were found to be in close proximity (10 μm or less) to each other

Automated Analysis of X-ray Images for Cargo Security

Customs and border officers are overwhelmed by the hundreds of millions of cargo containers that constitute the backbone of the global supply chain, any one of which could contain a security- or customs-related threat. Searching for these threats is akin to searching for needles in an ever-growing field of haystacks. This thesis considers novel automated image analysis methods to automate or assist elements of cargo inspection. The four main contributions of this thesis are as follows. Methods are proposed for the measurement and correction of detector wobble in large-scale transmission radiography using Beam Position Detectors (BPDs). Wobble is estimated from BPD measurements using a Random Regression Forest (RRF) model, Bayesian fused with a prior estimate from an Auto-Regression (AR). Next, a series of image corrections are derived, and it is shown that 87% of image error due to wobble can be corrected. This is the first proposed method for correction of wobble in large-scale transmission radiography. A Threat Image Projection (TIP) framework is proposed, for training, probing and evaluating Automated Threat Detection (ATD) algorithms. The TIP method is validated experimentally, and a method is proposed to test whether algorithms can learn to exploit TIP artefacts. A system for Empty Container Verification (ECV) is proposed. The system, trained using TIP, is based on Random Forest (RF) classification of image patches according to fixed geometric features and container location. The method outperforms previous reported results, and is able to detect very small amounts of synthetically concealed smuggled contraband. Finally, a method for ATD is proposed, based on a deep Convolutional Neural Network (CNN), trained from scratch using TIP, and exploits the material information encoded within dual-energy X-ray images to suppress false alarms. The system offers a 100-fold improvement in the false positive rate over prior work

Radon Transform based Metal Artefacts Generation in 3D Threat Image Projection

Threat Image Projection (TIP) plays an important role in aviation security. In order to evaluate human security screeners in determining threats, TIP systems project images of realistic threat items into the images of the passenger baggage being scanned. In this proof of concept paper, we propose a 3D TIP method which can be integrated within new 3D Computed Tomography (CT) screening systems. In order to make the threat items appear as if they were genuinely located in the scanned bag, appropriate CT metal artefacts are generated in the resulting TIP images according to the scan orientation, the passenger bag content and the material of the inserted threat items. This process is performed in the projection domain using a novel methodology based on the Radon Transform. The obtained results using challenging 3D CT baggage images are very promising in terms of plausibility and realism. 1