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Recent Advances in Computational Intelligence in Defense and Security pp 13–44Cite as

Computational Intelligence Methods in Forward-Looking Explosive Hazard Detection

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 621))

Abstract

This chapter discusses several methods for forward-looking (FL) explosive hazard detection (EHD) using FL infrared (FLIR) and FL ground penetrating radar (FLGPR). The challenge in detecting explosive hazards with FL sensors is that there are multiple types of targets buried at different depths in a highly-cluttered environment. A wide array of target and clutter signatures exist, which makes detection algorithm design difficult. Recent work in this application has focused on fusion methods, including fusion of multiple modalities of sensors (e.g., GPR and IR), fusion of multiple frequency sub-band images in FLGPR, and feature-level fusion using multiple kernel and iECO learning. For this chapter, we will demonstrate several types of EHD techniques, including kernel methods such as support vector machines (SVMs), multiple kernel learning MKL, and feature learning methods, including deep learners and iECO learning. We demonstrate the performance of several algorithms using FLGPR and FLIR data collected at a US Army test site. The summary of this work is that deep belief networks and evolutionary approaches to feature learning were shown to be very effective both for FLGPR and FLIR based EHD.

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Acknowledgments

This work is funded in part by a National Institute of Justice grant (2011-DN-BX-K838), U.S. Army (W909MY-13-C0013, W909MY-13-C0029) and Army Research Office (W911NF-14-1-0114 and 57940-EV) in support of the U.S. Army RDECOM CERDEC NVESD. Superior, a high performance computing cluster at Michigan Technological University, was used in obtaining results presented in this work.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering/Department of Computer Science, Michigan Technological University, Houghton, MI, 49931, USA

    Timothy C. Havens, John Becker & Anthony J. Pinar

  2. Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS, USA

    Derek T. Anderson

  3. Department of Electrical and Computer Engineering, University of Missouri, Columbia, MO, USA

    Kevin Stone

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  1. Timothy C. Havens
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Correspondence to Timothy C. Havens .

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Editors and Affiliations

  1. Larus Technologies Corporation, Ottawa, Ontario, Canada

    Rami Abielmona

  2. Larus Technologies Corporation, Ottawa, Ontario, Canada

    Rafael Falcon

  3. Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada

    Nur Zincir-Heywood

  4. School of Engineering and Information Technology, University of New South Wales, Canberra, Aust Capital Terr, Australia

    Hussein A. Abbass

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Havens, T.C., Anderson, D.T., Stone, K., Becker, J., Pinar, A.J. (2016). Computational Intelligence Methods in Forward-Looking Explosive Hazard Detection. In: Abielmona, R., Falcon, R., Zincir-Heywood, N., Abbass, H. (eds) Recent Advances in Computational Intelligence in Defense and Security. Studies in Computational Intelligence, vol 621. Springer, Cham. https://doi.org/10.1007/978-3-319-26450-9_2

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  • DOI: https://doi.org/10.1007/978-3-319-26450-9_2

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