Synthetic threat injection using digital twin informed augmentation

Daniel Krofcheck; Esther John; Hugh Galloway; Asael Sorensen; Carter Jameson; Connor Aubry; Arvind Prasadan; Robert Forrest

doi:10.1117/12.2618972

3 June 2022 Synthetic threat injection using digital twin informed augmentation

Daniel Krofcheck, Esther John, Hugh Galloway, Asael Sorensen, Carter Jameson, Connor Aubry, Arvind Prasadan, Robert Forrest

Author Affiliations +

Proceedings Volume 12104, Anomaly Detection and Imaging with X-Rays (ADIX) VII; 1210407 (2022) https://doi.org/10.1117/12.2618972
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

The growing x-ray detection burden for vehicles at Ports of Entry in the US requires the development of efficient and reliable algorithms to assist human operator in detecting contraband. Developing algorithms for large-scale non-intrusive inspection (NII) that both meet operational performance requirements and are extensible for use in an evolving environment requires large volumes and varieties of training data, yet collecting and labeling data for these enivornments is prohibitively costly and time consuming. Given these, generating synthetic data to augment algorithm training has been a focus of recent research. Here we discuss the use of synthetic imagery in an object detection framework, and describe a simulation based approach to determining domain-informed threat image projection (TIP) augmentation.

Conference Presentation

Citation Download Citation

Daniel Krofcheck, Esther John, Hugh Galloway, Asael Sorensen, Carter Jameson, Connor Aubry, Arvind Prasadan, and Robert Forrest "Synthetic threat injection using digital twin informed augmentation", Proc. SPIE 12104, Anomaly Detection and Imaging with X-Rays (ADIX) VII, 1210407 (3 June 2022); https://doi.org/10.1117/12.2618972

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