Publications Details

Resilient adjudication in non-intrusive inspection with hierarchical object and anomaly detection

Krofcheck, Daniel J.; John, Esther W.; Galloway, Hugh M.; Sorensen, Asael H.; Jameson, Carter D.; Aubry, Connor; Prasadan, Arvind P.; Galasso, Jennifer G.; Goodman, Eric G.; Forrest, Robert F.

Large scale non-intrusive inspection (NII) of commercial vehicles is being adopted in the U.S. at a pace and scale that will result in a commensurate growth in adjudication burdens at land ports of entry. The use of computer vision and machine learning models to augment human operator capabilities is critical in this sector to ensure the flow of commerce and to maintain efficient and reliable security operations. The development of models for this scale and speed requires novel approaches to object detection and novel adjudication pipelines. Here we propose a notional combination of existing object detection tools using a novel ensembling framework to demonstrate the potential for hierarchical and recursive operations. Further, we explore the combination of object detection with image similarity as an adjacent capability to provide post-hoc oversight to the detection framework. The experiments described herein, while notional and intended for illustrative purposes, demonstrate that the judicious combination of diverse algorithms can result in a resilient workflow for the NII environment.