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

Robert Forrest; Daniel Krofcheck; Asael Sorensen; Carter Jameson; Connor Aubry; Eion Blanchard

doi:10.1117/12.2666138

14 June 2023 Developing a comprehensive, adaptive system for large-scale x-ray images

Robert Forrest, Daniel Krofcheck, Asael Sorensen, Carter Jameson, Connor Aubry, Eion Blanchard

Proceedings Volume 12531, Anomaly Detection and Imaging with X-Rays (ADIX) VIII; 1253109 (2023) https://doi.org/10.1117/12.2666138
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Inspection of commerce entering the country is currently a burdensome process, assisted greatly by non-intrusive inspection (NII) technologies such as X-Ray imaging. There are a variety of singular ML techniques that assist in the adjudication process, many are well known, common and widely available with proper data. However, the challenge is to develop a comprehensive system exhibiting certain properties that leverages many techniques simultaneously and combines results to improve overall performance. Here we describe the concepts, components, data and implementation of a system we use for NII adjudication that is well suited to adapting to future needs. We demonstrate several components of this system the benefits of it. Specifically, we show the combination of results from common methods achieves superior performance in concert. First, ensembeling has proven successful in combining results from a variety of object detection models. We show the impact of using our novel graph clique-based method to increase performance over independent models. Additionally, because of performant and reliable object detection results, we demonstrate how constructing an image level contextualization of a scene allows analysis at the metadata level. Lastly, we leverage variational autoencoder (VAE) based similarity models to describe anomalies. We deploy these algorithms in a hierarchical fashion, developing optical signatures for whole NII scans as well as their components (e.g, entire vehicle, trunk, wheels). Here, we quantify the impacts of the inclusion of these methods in image anomaly detection task and demonstrate them in the extensible framework we have developed for NII applications. This paper will serve as an overview of our approach and show results from both combinatorial techniques.

Conference Presentation

Citation Download Citation

Robert Forrest, Daniel Krofcheck, Asael Sorensen, Carter Jameson, Connor Aubry, and Eion Blanchard "Developing a comprehensive, adaptive system for large-scale x-ray images", Proc. SPIE 12531, Anomaly Detection and Imaging with X-Rays (ADIX) VIII, 1253109 (14 June 2023); https://doi.org/10.1117/12.2666138

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available