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.
|