Presentation
14 June 2023 Applying AI techniques for detecting strokes in the brain to detecting guns, knives, and explosives in bags
Thomas Anthony, William Monroe, Frank Skidmore
Author Affiliations +
Abstract
We are actively developing methods for augmenting the effectiveness of CT based Explosives Detection Systems (EDS) screening. Our prototype solutions indicate capabilities of improving the security, throughput, and/or TSO deployments at airports of all sizes. Through a collaboration with Sandia National Laboratories, we have developed AI-based explosive detection Automated Threat Recognition Algorithms (ATRs) that can detect both solid and liquid explosives. We leverage the capabilities of deep learning to combine effective atomic number (Z-Eff) and object density, as well as multi-perspective 3D information to precisely localize areas of concerns in luggage. A reduction in false positive detections, and reduced TSA inspection burden is anticipated with the algorithms acting as force multiplier improving security effectiveness. Deep-learning therefore has the potential to revolutionize airport screening. In an airport screening environment, effective implementation of deep-learning will lead to dramatic improvements in efficiency, lower false positive rates, and fewer missed detections.
Conference Presentation
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Thomas Anthony, William Monroe, and Frank Skidmore "Applying AI techniques for detecting strokes in the brain to detecting guns, knives, and explosives in bags", Proc. SPIE 12531, Anomaly Detection and Imaging with X-Rays (ADIX) VIII, 1253106 (14 June 2023); https://doi.org/10.1117/12.2666142
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