Detection of concealed cars in complex cargo X-ray imagery using Deep Learning

BACKGROUND: Non-intrusive inspection systems based on X-ray radiography techniques are routinely used at transport hubs to ensure the conformity of cargo content with the supplied shipping manifest. As trade volumes increase and regulations become more stringent, manual inspection by trained operators is less and less viable due to low throughput. Machine vision techniques can assist operators in their task by automating parts of the inspection workflow. Since cars are routinely involved in trafficking, export fraud, and tax evasion schemes, they represent an attractive target for automated detection and flagging for subsequent inspection by operators. OBJECTIVE: Development and evaluation of a novel method for the automated detection of cars in complex X-ray cargo imagery. METHODS: X-ray cargo images from a stream-of-commerce dataset were classified using a window-based scheme. The limited number of car images was addressed by using an oversampling scheme. Different Convolutional Neural Network (CNN) architectures were compared with well-established bag of words approaches. In addition, robustness to concealment was evaluated by projection of objects into car images. RESULTS: CNN approaches outperformed all other methods evaluated, achieving 100% car image classification rate for a false positive rate of 1-in-454. Cars that were partially or completely obscured by other goods, a modus operandi frequently adopted by criminals, were correctly detected. CONCLUSIONS: We believe that this level of performance suggests that the method is suitable for deployment in the field. It is expected that the generic object detection workflow described can be extended to other object classes given the availability of suitable training data

Tackling the X-ray cargo inspection challenge using machine learning

The current infrastructure for non-intrusive inspection of cargo containers cannot accommodate exploding com-merce volumes and increasingly stringent regulations. There is a pressing need to develop methods to automate parts of the inspection workflow, enabling expert operators to focus on a manageable number of high-risk images. To tackle this challenge, we developed a modular framework for automated X-ray cargo image inspection. Employing state-of-the-art machine learning approaches, including deep learning, we demonstrate high performance for empty container verification and specific threat detection. This work constitutes a significant step towards the partial automation of X-ray cargo image inspection

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

Transferring X-ray based automated threat detection between scanners with different energies and resolution

A significant obstacle to developing high performance Deep Learning algorithms for Automated Threat Detection (ATD) in security X-ray imagery, is the difficulty of obtaining large training datasets. In our previous work, we circumvented this problem for ATD in cargo containers, using Threat Image Projection and data augmentation. In this work, we investigate whether data scarcity for other modalities, such as parcels and baggage, can be ameliorated by transforming data from one domain so that it approximates the appearance of another. We present an ontology of ATD datasets to assess where transfer learning may be applied. We define frameworks for transfer at the training and testing stages, and compare the results for both methods against ATD where a common data source is used for training and testing. Our results show very poor transfer, which we attribute to the difficulty of accurately matching the blur and contrast characteristics of different scanners

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