KEYWORDS: Data modeling, X-rays, Education and training, Systems modeling, Process modeling, Design and modelling, Artificial intelligence, Virtual reality
Synthetic data has emerged as a critical piece of the machine-learning based approach to X-ray system design and automatic threat recognition development. Physics-based synthetic data integrates virtual models with a physics-based simulation engine, thereby granting users the capability to produce synthetic measurements based on arbitrary, user-specified input objects and materials. Such inputs can range from geometric phantoms that assist in system design to new threat materials and configurations that expedite ATR training in response to emerging threats. We introduce enhancements to the QSimRT virtual model generation pipeline. This incorporates the rapid creation of virtual models representing passenger luggage, stochastically generated electronics, and user-specified model variability for extensive ensemble production. We have employed these models in training ATRs within the aviation security domain. This presentation will discuss the model generation process, emphasize its pivotal features, and share preliminary results derived from the application of these models in ATR training.
Identifying and intercepting prohibited items and explosives is a critical focus of aviation security. While computed tomography (CT) systems represent the industry standard for detecting explosives in baggage, x-ray diffraction imaging (XRDI) systems have shown increasing performance and commercial viability. Our approach to explosives detection involves the combination of CT and XRDI into a single, hybrid system where both the CT and XRDI data are utilized in the reconstruction and classification algorithms. In this work, we focus on comparing multiple reconstruction and classifier implementations and quantifying the resulting performance. Our analysis shows higher quality reconstructions lead to improved material separability, better classification performance (detection and false alarm rates), and reduces model uncertainty. Through this work, we demonstrate the relationship between improved quality of reconstructions and the separability of threat from non-threat objects in the domain of explosives detection.
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