Improving Non-Destructive Detection Technology Through SAVY Feature Detection

Abstract

Surveillance of special nuclear material (SNM) storage containers is required by the DOE to assess their integrity across the Complex. This work aims to improve the task of container inspections by leveraging automation through machine learning (ML) tools to reduce the human-intensive effort and expert-level knowledge needed to assess container status. A field-deployable, non-destructive technology was designed using off-the-shelf components to collect multiple images from different perspectives of containers in storage to detect both spatial features of interest and anomalies of concern. Nine ML models were generated using unique training datasets and parameters. Learned features include SAVY surface regions including the body side wall, collar, lid, filter, and printed/etched information. Average Precision (AP) is used to calculate detection performance when both viewing previously seen environments and previously unseen environments. The application of image transformations and resolution scaling while training greatly improved the detection performance in unseen environments, and significantly increasing the number of computation iterations improved detection performance on previously seen environments. Additional capabilities were developed including the novel detection of procedural non-compliance and the ability to localize anomalies relative to SAVY surface features.