Developing a comprehensive, adaptive system for large scale x-ray images

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

doi:10.1117/12.2619020

3 June 2022 Developing a comprehensive, adaptive system for large scale x-ray images

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

Author Affiliations +

Proceedings Volume 12104, Anomaly Detection and Imaging with X-Rays (ADIX) VII; 1210408 (2022) https://doi.org/10.1117/12.2619020
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Inspection of commerce entering the country is currently an onerous, custom and extremely labor-intensive process. The imaging of commerce with 2D x-rays aids the ability to interdict certain items entering the country, reducing risk and increasing national security. We use a variety of ML techniques together to develop automated threat recognition (ATR) algorithms to assist this process. The challenge is to develop a system that incorporates several approaches simultaneously and ensemble results to improve overall performance. We employ several algorithmic techniques to solve the problem. Generally, we have synthetic data generation techniques that can rapidly spin up datasets that are then employed to train ML models. GANs are used to refine the synthetic data to resemble reality more faithfully; thresholding objectively improves top line S/N; semi-supervised methods are used to handle data sparsity and leverage any available unlabeled stream of commerce (SOC) data. Suites of object detectors infer in parallel using common techniques and results are then ensembled using a novel graph-based method. Similarity and change detection along with topological data analysis may leverage historical data to investigate anomalies. This paper is an overview of our approach, generating synthetic data and attempting to unite these ML methods to provide the highest performance for ATR of 2D x-ray data.

Conference Presentation

Citation Download Citation

Robert Forrest, Daniel Krofcheck, Esther John, Hugh Galloway, Asael Sorensen, Carter Jameson, Connor Aubry, Arvind Prasadan, Jennifer Galasso, and Eric Goodman "Developing a comprehensive, adaptive system for large scale x-ray images", Proc. SPIE 12104, Anomaly Detection and Imaging with X-Rays (ADIX) VII, 1210408 (3 June 2022); https://doi.org/10.1117/12.2619020

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