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

‘Unexpected item in the bagging area’: Anomaly Detection in X-ray Security Images

The role of Anomaly Detection in X-ray security imaging, as a supplement to targeted threat detection, is described; and a taxonomy of anomalies types in this domain is presented. Algorithms are described for detecting appearance anomalies, of shape, texture and density; and semantic anomalies of object category presence. The anomalies are detected on the basis of representations extracted from a convolutional neural network pre-trained to identify object categories in photographs: from the final pooling layer for appearance anomalies, and from the logit layer for semantic anomalies. The distribution of representations in normal data are modelled using high-dimensional, full-covariance, Gaussians; and anomalies are scored according to their likelihood relative to those models. The algorithms are tested on X-ray parcel images using stream-of-commerce data as the normal class, and parcels with firearms present as examples of anomalies to be detected. Despite the representations being learnt for photographic images, and the varied contents of stream-of-commerce parcels; the system, trained on stream-of-commerce images only, is able to detect 90% of firearms as anomalies, while raising false alarms on 18% of stream-of-commerce

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