Automated detection of common IED components on resource constrained computing devices

Emma Garvey; Anna Svagzdys; Richard W. Demar; Gregory F. S. Hewitt; Philip J. Stimac

doi:10.1117/12.2618506

27 May 2022 Automated detection of common IED components on resource constrained computing devices

Emma Garvey, Anna Svagzdys, Richard W. Demar, Gregory F. S. Hewitt, Philip J. Stimac

Proceedings Volume 12102, Real-Time Image Processing and Deep Learning 2022; 1210202 (2022) https://doi.org/10.1117/12.2618506
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Developing automated threat detection algorithms for imaging equipment used by explosive ordnance disposal (EOD) and public safety personnel has the potential to improve mission efficiency and safety by automatically drawing a user’s attention to potential threats. To demonstrate the value of automated threat detection algorithms to the EOD community, Deep Analytics LLC (DA) developed an object detection algorithm that runs in real-time on resource constrained devices. The object detection algorithm identifies 10 common classes of improvised explosive device (IED) components in live video and alerts a user when an IED component is detected. In this paper we discuss the development of the IED component dataset, the training and evaluation of the object detection algorithm, and the deployment on the algorithm on resource constrained hardware.

Conference Presentation

Citation Download Citation

Emma Garvey, Anna Svagzdys, Richard W. Demar, Gregory F. S. Hewitt, and Philip J. Stimac "Automated detection of common IED components on resource constrained computing devices", Proc. SPIE 12102, Real-Time Image Processing and Deep Learning 2022, 1210202 (27 May 2022); https://doi.org/10.1117/12.2618506

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