Explosive hazard pre-screener based on simulated data with perfect annotation and imprecisely labeled real data

Madeline Kovaleski; Aaron Fuller; Jeffrey Kerley; Brendan J. Alvey; Peter Popescu; Derek Anderson; Andrew Buck; James Keller; Grant Scott; Clare Yang; Ken E. Yasuda; Hollie A. Ryan

doi:10.1117/12.2618792

30 May 2022 Explosive hazard pre-screener based on simulated data with perfect annotation and imprecisely labeled real data

Madeline Kovaleski, Aaron Fuller, Jeffrey Kerley, Brendan J. Alvey, Peter Popescu, Derek Anderson, Andrew Buck, James Keller, Grant Scott, Clare Yang, Ken E. Yasuda, Hollie A. Ryan

Author Affiliations +

Proceedings Volume 12116, Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XXIII; 121160X (2022) https://doi.org/10.1117/12.2618792
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Datasets with accurate ground truth from unmanned aerial vehicles (UAV) are cost and time prohibitive. This is a problem as most modern machine learning (ML) algorithms are based on supervised learning and require large and diverse well-annotated datasets. As a result, new creative ideas are needed to drive innovation in robust and trustworthy artificial intelligence (AI) / ML. Herein, we use the Unreal Engine (UE) to generate simulated visual spectrum imagery for explosive hazard detection (EHD) with corresponding pixel-level labels, UAV metadata, and environment metadata. We also have access to a relatively small set of real world EH data with less precise ground truth – axis aligned bounding box labels – and sparse metadata. In this article, we train a lightweight, real-time, pixel-level EHD pre-screener for a low-altitude UAV. Specifically, we focus on training with respect to different combinations of simulated and real data. Encouraging preliminary results are provided relative to real world EH data. Our findings suggest that while simulated data can be used to augment limited volume and variety real world data, it could perhaps be sufficient by itself to train an EHD pre-screener.

Conference Presentation

Citation Download Citation

Madeline Kovaleski, Aaron Fuller, Jeffrey Kerley, Brendan J. Alvey, Peter Popescu, Derek Anderson, Andrew Buck, James Keller, Grant Scott, Clare Yang, Ken E. Yasuda, and Hollie A. Ryan "Explosive hazard pre-screener based on simulated data with perfect annotation and imprecisely labeled real data", Proc. SPIE 12116, Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XXIII, 121160X (30 May 2022); https://doi.org/10.1117/12.2618792

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