Threat identification in humanitarian demining using machine learning and spectroscopic metal detection

Wouter van Verre; Toykan Özdeǧer; Ananya Gupta; Frank J.W. Podd; Anthony J. Peyton

doi:10.1007/978-3-030-33607-3_58

Threat identification in humanitarian demining using machine learning and spectroscopic metal detection

Wouter van Verre^*
, Toykan Özdeǧer
, Ananya Gupta
, Frank J.W. Podd
, Anthony J. Peyton

^*Corresponding author for this work

UMIST

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

Abstract

The detection of buried minimum-metal anti-personnel landmines is a time-consuming problem, due to the high false alarm rate (FAR) arising from metallic clutter typically found in minefields. Magnetic induction spectroscopy (MIS) offers a potential way to reduce the FAR by classifying the metallic objects into threat and non-threat categories, based on their spectroscopic signatures. A new algorithm for threat identification for MIS sensors, based on a fully-connected artificial neural network (ANN), is proposed in this paper, and compared against a classifier based on Support Vector Machines (SVM). The results demonstrate that MIS is a potentially viable option for the reduction of false alarms in humanitarian demining. It is also shown that the ANN outperforms the SVM-based approach for threat objects containing minimal amounts of metal.

Original language	English
Title of host publication	Intelligent Data Engineering and Automated Learning – IDEAL 2019 - 20th International Conference, Proceedings
Editors	Hujun Yin, Richard Allmendinger, David Camacho, Peter Tino, Antonio J. Tallón-Ballesteros, Ronaldo Menezes
Publisher	Springer Nature
Pages	542-549
Number of pages	8
ISBN (Print)	9783030336066
DOIs	https://doi.org/10.1007/978-3-030-33607-3_58
Publication status	Published - 1 Jan 2019
Event	20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019 - Manchester, United Kingdom Duration: 14 Nov 2019 → 16 Nov 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11871 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019
Country/Territory	United Kingdom
City	Manchester
Period	14/11/19 → 16/11/19

Keywords

Landmine detection
Machine learning
Magnetic induction spectroscopy

Research Beacons, Institutes and Platforms

Dalton Nuclear Institute

Access to Document

10.1007/978-3-030-33607-3_58

Cite this

van Verre, W., Özdeǧer, T., Gupta, A., Podd, F. J. W., & Peyton, A. J. (2019). Threat identification in humanitarian demining using machine learning and spectroscopic metal detection. In H. Yin, R. Allmendinger, D. Camacho, P. Tino, A. J. Tallón-Ballesteros, & R. Menezes (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2019 - 20th International Conference, Proceedings (pp. 542-549). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11871 LNCS). Springer Nature. https://doi.org/10.1007/978-3-030-33607-3_58

van Verre, Wouter ; Özdeǧer, Toykan ; Gupta, Ananya et al. / Threat identification in humanitarian demining using machine learning and spectroscopic metal detection. Intelligent Data Engineering and Automated Learning – IDEAL 2019 - 20th International Conference, Proceedings. editor / Hujun Yin ; Richard Allmendinger ; David Camacho ; Peter Tino ; Antonio J. Tallón-Ballesteros ; Ronaldo Menezes. Springer Nature, 2019. pp. 542-549 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{f07e288b4f2d4a469156b11ec5953254,

title = "Threat identification in humanitarian demining using machine learning and spectroscopic metal detection",

abstract = "The detection of buried minimum-metal anti-personnel landmines is a time-consuming problem, due to the high false alarm rate (FAR) arising from metallic clutter typically found in minefields. Magnetic induction spectroscopy (MIS) offers a potential way to reduce the FAR by classifying the metallic objects into threat and non-threat categories, based on their spectroscopic signatures. A new algorithm for threat identification for MIS sensors, based on a fully-connected artificial neural network (ANN), is proposed in this paper, and compared against a classifier based on Support Vector Machines (SVM). The results demonstrate that MIS is a potentially viable option for the reduction of false alarms in humanitarian demining. It is also shown that the ANN outperforms the SVM-based approach for threat objects containing minimal amounts of metal.",

keywords = "Landmine detection, Machine learning, Magnetic induction spectroscopy",

author = "\{van Verre\}, Wouter and Toykan {\"O}zdeǧer and Ananya Gupta and Podd, \{Frank J.W.\} and Peyton, \{Anthony J.\}",

year = "2019",

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doi = "10.1007/978-3-030-33607-3\_58",

language = "English",

isbn = "9783030336066",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Nature",

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booktitle = "Intelligent Data Engineering and Automated Learning – IDEAL 2019 - 20th International Conference, Proceedings",

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note = "20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019 ; Conference date: 14-11-2019 Through 16-11-2019",

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van Verre, W, Özdeǧer, T, Gupta, A, Podd, FJW & Peyton, AJ 2019, Threat identification in humanitarian demining using machine learning and spectroscopic metal detection. in H Yin, R Allmendinger, D Camacho, P Tino, AJ Tallón-Ballesteros & R Menezes (eds), Intelligent Data Engineering and Automated Learning – IDEAL 2019 - 20th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11871 LNCS, Springer Nature, pp. 542-549, 20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019, Manchester, United Kingdom, 14/11/19. https://doi.org/10.1007/978-3-030-33607-3_58

Threat identification in humanitarian demining using machine learning and spectroscopic metal detection. / van Verre, Wouter; Özdeǧer, Toykan; Gupta, Ananya et al.
Intelligent Data Engineering and Automated Learning – IDEAL 2019 - 20th International Conference, Proceedings. ed. / Hujun Yin; Richard Allmendinger; David Camacho; Peter Tino; Antonio J. Tallón-Ballesteros; Ronaldo Menezes. Springer Nature, 2019. p. 542-549 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11871 LNCS).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Threat identification in humanitarian demining using machine learning and spectroscopic metal detection

AU - van Verre, Wouter

AU - Özdeǧer, Toykan

AU - Gupta, Ananya

AU - Podd, Frank J.W.

AU - Peyton, Anthony J.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - The detection of buried minimum-metal anti-personnel landmines is a time-consuming problem, due to the high false alarm rate (FAR) arising from metallic clutter typically found in minefields. Magnetic induction spectroscopy (MIS) offers a potential way to reduce the FAR by classifying the metallic objects into threat and non-threat categories, based on their spectroscopic signatures. A new algorithm for threat identification for MIS sensors, based on a fully-connected artificial neural network (ANN), is proposed in this paper, and compared against a classifier based on Support Vector Machines (SVM). The results demonstrate that MIS is a potentially viable option for the reduction of false alarms in humanitarian demining. It is also shown that the ANN outperforms the SVM-based approach for threat objects containing minimal amounts of metal.

AB - The detection of buried minimum-metal anti-personnel landmines is a time-consuming problem, due to the high false alarm rate (FAR) arising from metallic clutter typically found in minefields. Magnetic induction spectroscopy (MIS) offers a potential way to reduce the FAR by classifying the metallic objects into threat and non-threat categories, based on their spectroscopic signatures. A new algorithm for threat identification for MIS sensors, based on a fully-connected artificial neural network (ANN), is proposed in this paper, and compared against a classifier based on Support Vector Machines (SVM). The results demonstrate that MIS is a potentially viable option for the reduction of false alarms in humanitarian demining. It is also shown that the ANN outperforms the SVM-based approach for threat objects containing minimal amounts of metal.

KW - Landmine detection

KW - Machine learning

KW - Magnetic induction spectroscopy

UR - https://www.scopus.com/pages/publications/85076643583

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DO - 10.1007/978-3-030-33607-3_58

M3 - Conference contribution

AN - SCOPUS:85076643583

SN - 9783030336066

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 542

EP - 549

BT - Intelligent Data Engineering and Automated Learning – IDEAL 2019 - 20th International Conference, Proceedings

A2 - Yin, Hujun

A2 - Allmendinger, Richard

A2 - Camacho, David

A2 - Tino, Peter

A2 - Tallón-Ballesteros, Antonio J.

A2 - Menezes, Ronaldo

PB - Springer Nature

T2 - 20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019

Y2 - 14 November 2019 through 16 November 2019

ER -

van Verre W, Özdeǧer T, Gupta A, Podd FJW , Peyton AJ. Threat identification in humanitarian demining using machine learning and spectroscopic metal detection. In Yin H, Allmendinger R, Camacho D, Tino P, Tallón-Ballesteros AJ, Menezes R, editors, Intelligent Data Engineering and Automated Learning – IDEAL 2019 - 20th International Conference, Proceedings. Springer Nature. 2019. p. 542-549. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-33607-3_58

Threat identification in humanitarian demining using machine learning and spectroscopic metal detection

Abstract

Publication series

Conference

Keywords

Research Beacons, Institutes and Platforms

Access to Document

Other files and links

Fingerprint

Electromagnetic Sensing Group

Reducing the Threat to Public Safety: Improved metallic object characterisation, location and detection

Cite this

Threat identification in humanitarian demining using machine learning and spectroscopic metal detection

Abstract

Publication series

Conference

Keywords

Research Beacons, Institutes and Platforms

Access to Document

Other files and links

Fingerprint

Projects

Electromagnetic Sensing Group

Reducing the Threat to Public Safety: Improved metallic object characterisation, location and detection

Cite this