2006 SPIE Defense and Security Symposium Gamma/X-Ray Linear Pushbroom Stereo for 3D Cargo Inspection

For evaluating the contents of trucks, containers, cargo, and passenger vehicles by a non-intrusive gamma-ray or X-ray imaging system to determine the possible presence of contraband, three-dimensional (3D) measurements could provide more information than 2D measurements. In this paper, a linear pushbroom scanning model is built for such a commonly used gamma-ray or x-ray cargo inspection system. Accurate 3D measurements of the objects inside a cargo can be obtained by using two such scanning systems with different scanning angles to construct a pushbroom stereo system. A simple but robust calibration method is proposed to find the important parameters of the linear pushbroom sensors. Then, a fast and automated stereo matching algorithm based on free-form deformable registration is developed to obtain 3D measurements of the objects under inspection. A user interface is designed for 3D visualization of the objects in interests. Experimental results of sensor calibration, stereo matching, 3D measurements and visualization of a 3D cargo container and the objects inside, are presented

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