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.
Supported by the Sir Bobby Charlton Foundation and EPSRC.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). https://www.tensorflow.org/
Amazeen, C., Locke, M.: Developmental status of the U.S. Army’s new Handheld STAndoff MIne Detection System (HSTAMIDS). In: Second International Conference on Detection of Abandoned Land Mines, vol. 1998, pp. 193–197 (1998)
Chollet, F., et al.: Keras (2015). https://keras.io
Daniels, D., Braustein, J., Nevard, M.: Using MINEHOUND in Cambodia and Afghanistan. J. ERW Mine Action 18(2), 14 (2014)
Daniels, D.J.: A review of GPR for landmine detection. Sens. Imaging: Int. J. 7(3), 90–123 (2006)
Geophex: GEM-3M: A Ground Imager with a Local Navigator. Technical report (2012)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016). http://www.deeplearningbook.org
Huang, H., Won, I.J.: Automated identification of buried landmines using normalized electromagnetic induction spectroscopy. IEEE Trans. Geosci. Remote Sens. 41(3), 640–651 (2003)
Huang, H., Won, I.J.: Characterization of UXO-like targets using broadband electromagnetic induction sensors. IEEE Trans. Geosci. Remote Sens. 41(3), 652–663 (2003)
Knox, M., Rundel, C., Collins, L.: Sensor fusion for buried explosive threat detection for handheld data. In: Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII 10182, May 2017. 101820D (2017). https://doi.org/10.1117/12.2263013
Lameri, S., Lombardi, F., Bestagini, P., Lualdi, M., Tubaro, S.: Landmine detection from GPR data using convolutional neural networks. In: 25th European Signal Processing Conference, EUSIPCO, January 2017, pp. 508–512 (2017). https://doi.org/10.23919/EUSIPCO.2017.8081259
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)
Marsh, L.A., et al.: Spectroscopic identification of anti-personnel mine surrogates from planar sensor measurements. In: Proceedings of IEEE Sensors, pp. 1–3 (2016)
Marsh, L.A., et al.: Combining electromagnetic spectroscopy and ground-penetrating radar for the detection of anti-personnel landmines. Sensors 19(15), 3390 (2019)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Sato, M., Kikuta, K., Chernyak, I.: Dual sensor “ALIS” for humanitarian demining. In: 2018 17th International Conference on Ground Penetrating Radar (GPR), pp. 1–4 (2018)
Stanley, R.J., Gader, P.D., Ho, K.C.: Feature and decision level sensor fusion of electromagnetic induction and ground penetrating radar sensors for landmine detection with hand-held units (2002)
The Halo Trust: HALO Utilises Dual-sensor Detector | The HALO Trust (2011). https://www.halotrust.org/media-centre/news/halo-utilises-dual-sensor-detector/
UN Secretary General: Assistance in mine clearance: Report of the Secretary-General A/49/357. Technical report, United Nations, September 1994
van Verre, W., Marsh, L.A., Davidson, J.L., Cheadle, E., Podd, F.J.W., Peyton, A.J.: Detection of Metallic Objects in Mineralised Soil Using Magnetic Induction Spectroscopy (2019, submitted)
Won, I.J., Keiswetter, D.A., Bell, T.H., Miller, J., Barrow, B.: Electromagnetic induction spectroscopy for landmine identification. IEEE Trans. Geosci. Remote Sens. 39(4), 801–809 (2001)
Won, I.J., Keiswetter, D.A., Hanson, D.R., Novikova, E., Hall, T.M.: GEM-3: Monostatic Broadband Electromagnetic Induction Sensor (1997)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
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: Yin, H., Camacho, D., Tino, P., Tallón-Ballesteros, A., Menezes, R., Allmendinger, R. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2019. IDEAL 2019. Lecture Notes in Computer Science(), vol 11871. Springer, Cham. https://doi.org/10.1007/978-3-030-33607-3_58
Download citation
DOI: https://doi.org/10.1007/978-3-030-33607-3_58
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33606-6
Online ISBN: 978-3-030-33607-3
eBook Packages: Computer ScienceComputer Science (R0)