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An Automated Decision System for Landmine Detection and Classification Using Metal Detector Signals

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Innovations in Defence Support Systems – 1

Part of the book series: Studies in Computational Intelligence ((SCI,volume 304))

Abstract

An automated decision system for landmine detection and discrimination is implemented and evaluated using metal detector array data. The techniques utilised include: a gradient based peak isolation method, wavelet transforms, fuzzy ARTMAP neural networks, and the generic majority voting scheme. The features selected for representing the input data are composed of the morphological and wavelet based features of the target signature responses. Classification experiments are conducted in an attempt to discriminate target type and burial depth according to two different methodologies. The results obtained are promising, with the implemented decision system achieving high probabilities of detection with reasonable false alarm rates, and exceptional discrimination before and after decision fusion with relatively low classification errors.

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Author information

Authors and Affiliations

  1. Knowledge-Based Intelligent Engineering Systems (KES) Centre, School of Electrical and Information Engineering, University of South Australia, Australia

    Minh Dao-Johnson Tran, Lakhmi C. Jain & C. P. Lim

  2. Threat Mitigation Group, Weapon Systems Division, Defence Science and Technology Organisation (DSTO), Edinburgh, Australia

    Canicious Abeynayake

Authors
  1. Minh Dao-Johnson Tran
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  2. Canicious Abeynayake
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  3. Lakhmi C. Jain
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  4. C. P. Lim
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Editor information

Editors and Affiliations

  1. School of Electrical and Information Engineering, University of South Australia, Adelaide, Mawson Lakes Campus, South Australia, Australia

    Anthony Finn  & Lakhmi C. Jain  & 

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Tran, M.DJ., Abeynayake, C., Jain, L.C., Lim, C.P. (2010). An Automated Decision System for Landmine Detection and Classification Using Metal Detector Signals. In: Finn, A., Jain, L.C. (eds) Innovations in Defence Support Systems – 1. Studies in Computational Intelligence, vol 304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14084-6_7

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  • DOI: https://doi.org/10.1007/978-3-642-14084-6_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14083-9

  • Online ISBN: 978-3-642-14084-6

  • eBook Packages: EngineeringEngineering (R0)

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