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International Conference on Advanced Intelligent Systems and Informatics

AISI 2020: Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2020 pp 126–136Cite as

Deep Neural Networks for Landmines Images Classification

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1261))

Abstract

This paper presents an efficient solution for automatic classification between the Anti-Tank (AT) landmines signatures and standard hyperbolic signatures obtained from other objects, including the Anti-personnel (AP) landmines based on pretrained deep Convolutional Neural Network (CNN). Specifically, two deep learning techniques have been tested and compared with another published landmine classification method. The first technique is based on VGG-16 pertained network for both features extraction and classification from the dataset of landmines images. While the second technique use Resnet-18 pretrained network for features extraction and Support Vector Machine (SVM) used for classification. The proposed algorithm has been tested using dataset of landmines images taken by the Laser Doppler Vibrometer based Acoustic to Seismic (LDV-A/S) landmine detection system. The results show that, the deep learning-based technique give higher classification accuracy than published landmine classification method. The Resnet-18 pretrained network-based and SVM classification gives better average accuracy than VGG-16 pertained network-based classification.

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Author information

Authors and Affiliations

  1. Nuclear Research Center, Atomic Energy Authority, P.O. 13759, Inshas, Cairo, Egypt

    Refaat M. Fikry & H. Kasban

Authors
  1. Refaat M. Fikry
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  2. H. Kasban
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Corresponding author

Correspondence to H. Kasban .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Artificial Intelligence, Information Technology Department, and Chair of the Scientific Research Group in Egypt, Cairo University, Cairo, Egypt

    Aboul Ella Hassanien

  2. Department of Electronics and Computer Science, Koszalin University of Technology, Koszalin, Poland

    Adam Slowik

  3. Faculty of Electrical Engineering and Computer Science, VŠB-Technical University of Ostrava, Ostrava-Poruba, Moravskoslezsky, Czech Republic

    Václav Snášel

  4. Rector of the Electronic Research Institute, Cairo, Egypt

    Hisham El-Deeb

  5. Faculty of computers and information, Ain Shams University, Cairo, Egypt

    Fahmy M. Tolba

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© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

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Fikry, R.M., Kasban, H. (2021). Deep Neural Networks for Landmines Images Classification. In: Hassanien, A.E., Slowik, A., Snášel, V., El-Deeb, H., Tolba, F.M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2020. AISI 2020. Advances in Intelligent Systems and Computing, vol 1261. Springer, Cham. https://doi.org/10.1007/978-3-030-58669-0_11

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