Synthetic Training Data for Explosive Detection Machine Learning Algorithms

Deep learning offers a powerful and extensible toolset to achieve or perhaps even exceed human-level accuracy for automatic object detection in stream of commerce data, and offers a path toward improving the effectiveness of scanners, reducing wait times, and radically increasing the accuracy of screening, where automatic object detection support is essential. A deep learning-based system for automatic detection is compelling, as it can be trained, never gets fatigued or distracted, and it can improve over time as more examples become available. However, in order to train deep machine learning-models to operate on 2D and 3D screening data, a significant quantity of high-quality ground-truth training examples are required. We propose SoCPhysics: A Stream-of-Commerce Physics-Based Data Generation Application, which combines state of the art physics-based modeling and simulation, extensible and performant 3D modeling and real-time physics libraries, light-weight Python code wrappers, and scalable container-based architectures to produce high-quality, physics-based x-ray and mmw synthetic training data for machine learning model training, verification and validation. The training data and subsequent machine learning models generated and supported via the SoCPhysics application will serve to support Nninvasive screening at speed that will provide comprehensive threat protection while helping to adapt security to the pace of life rather than life to security. Furthermore, this project provides fundamental support toward realizing uNbtrusive screening of people, baggage, and cargo, and will help enable the seamless detection of threats while respecting privacy, with minimal impact to the pace of travel and speed of commerce.