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.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.