Skip to main content
SpringerLink
Log in

An Automatic Detection of Military Objects and Terrorism Classification System Based on Deep Transfer Learning

  • Conference paper
  • First Online:
Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020) (AICV 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1153))

Abstract

This paper presents an automated detection of military objects and terrorism classification system based on deep transfer learning. It constructs a new structure of neural networks based on AlexNet pre-trained transfer learning model. This network is designed by six neural networks for six spectrums (Intensified visual images, Near-infrared spectroscopy (NIR) images, thermal images, LWIR (long wave infrared images), DHV, and RGB). It uses for detecting objects and actions in various spectrums in night mode depends on two sensory data types of images and videos. The system proposes an automated description layer for improving the classification domain whether military or terrorism domain. The detection and classification result reaches 92% for objects and actions detection and classifying the compatible domains whether terrorism or military. The experiments are applied to three datasets due to the lack of critical data (images or videos) in these domains. This dataset reaches 7992 images in multiple spectrums.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Gohar, F., Haider, W., Qamar, U.: Terrorist group prediction using data classification. In: The International Conference on Artificial Intelligence and Pattern Recognition (AIPR 2014). Asia Pacific University of Technology and Innovation (APU) (2014)

    Google Scholar 

  2. Tinguria, D., Kumar, B.: Detecting terror- related activities on the web using neural network. Orient. J. Comput. Sci. Technol. 3(2), 331–336 (2010)

    Google Scholar 

  3. Ritchie, H., Hasell, J., Appel, C., Roser, M.: Terrorism. OurWorldInData.org (2019). https://ourworldindata.org/terrorism

  4. Institute for Economics and Peace: Global Terrorism Index 2019, Measuring the Impact of Terrorism, Sydney (2019). http://visionofhumanity.org/reports

  5. Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)

    Article  Google Scholar 

  6. Nascimento, I., et al.: Deep learning in RAT and modulation classification with a new radio signals dataset. In: Simpósio brasileiro de telecomunicações e processamento de sinais - sbrt, 16–19 de setembro de 2018

    Google Scholar 

  7. Yang, J., et al.: Hyperspectral image classification using two-channel deep convolutional neural network. In: Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, pp. 5079–5082 (2016)

    Google Scholar 

  8. Mormont, R., Geurts, P., Mar, R.: Comparison of deep transfer learning strategies for digital pathology. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2018)

    Google Scholar 

  9. Patil, C.J., Shinde, S.V.: Object detection in military and space image by deep learning with convolutional neural network. Int. J. Comput. Sci. Eng. 6(11), 363–368 (2018)

    Google Scholar 

  10. Yang, Z., et al.: Deep transfer learning for military object recognition under small training set condition. Neural Comput. Appl. 31(10), 6469–6478 (2018)

    Article  Google Scholar 

  11. Remjee, S., Yang, D., Elgamal, A.: Fast deep learning for automatic modulation classification. arXiv:1901.05850v1 (2019)

  12. Wu, Y., Ji, Q.: Constrained deep transfer feature learning and its applications. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5101–5109 (2016)

    Google Scholar 

  13. Liu, X., Liu, Z.-T., Wang, G.: Ensemble transfer learning algorithm. IEEE Access 6, 2389–2396 (2017)

    Article  Google Scholar 

  14. Wang, S., et al.: Alcoholism identification based on an AlexNet transfer learning model. Front. Psychiatry 10, 205 (2019)

    Article  Google Scholar 

  15. Singh, D., Garzon, P.: Using Convolutional Neural Networks and Transfer Learning to Perform Yelp Restaurant Photo Classification (2018)

    Google Scholar 

  16. Smith, P., Chen, C.: Transfer learning with deep CNNs for gender recognition and age estimation. arXiv:1811.07344v1 (2018)

  17. Hussain, M., Bird, J.J., Faria, D.R.: A study on CNN transfer learning for image classification. In: 18th Annual UK Workshop on Computational Intelligence (UKCI), Nottingham (2018)

    Google Scholar 

  18. Kwan, H.K., Lee, C.K.: A neural network approach to pulse radar detection. IEEE Trans. Aerosp. Electron. Syst. 29(1), 9–21 (1993)

    Article  Google Scholar 

  19. Talo, M., et al.: Application of deep transfer learning for automated brain abnormality classification using MR images. Cogn. Syst. Res. 54, 176–188 (2018)

    Article  Google Scholar 

  20. Almisreb, A.A., Jamil, N., Din, N.Md.: Utilizing AlexNet deep transfer learning for ear recognition. In: Fourth International Conference on Information Retrieval and Knowledge Management (CAMP) (2018)

    Google Scholar 

  21. Signoroni, A., et al.: Deep learning meets hyperspectral image analysis: a multidisciplinary review. J. Imaging 5, 52 (2019)

    Article  Google Scholar 

  22. Zhan, Y., et al.: Hyperspectral band selection based on deep convolutional neural network and distance density. IEEE Geosci. Remote Sens. Lett. 14, 2365–2369 (2017)

    Article  Google Scholar 

  23. Zhao, W., et al.: On combining multiscale deep learning features for the classification of hyperspectral remote sensing imagery. Int. J. Remote Sens. 36, 3368–3379 (2015)

    Article  Google Scholar 

  24. Toet, A., Pinkus, A.R., Hogervost, M.A.: The TRICLOBS dynamic multi-band image data set for the development and evaluation of image fusion. PLoS ONE 11, e0165016 (2016)

    Article  Google Scholar 

  25. Chandan, J.G., et al.: Real time object detection and tracking using Deep Learning and OpenCV. In: International Conference on Inventive Research in Computing Applications (ICIRCA) (2018)

    Google Scholar 

  26. Zukal, M., et al.: Interest points as a focus measure in multi-spectral imaging. Radio Eng. 22(1), 68–81 (2013)

    Google Scholar 

  27. Khalifa, N.E.M., et al.: Deep iris: deep learning for gender classification through iris patterns. Acta Informatica Medica 27(2), 96–102 (2019)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Information Systems Department, Faculty of Computers and Artificial Intelligence, Cairo University, Cairo, Egypt

    Doaa Mohey El-Din & Ehab E. Hassanien

  2. Information Technology Department, Faculty of Computers and Artificial Intelligence, Cairo University, Cairo, Egypt

    Aboul Ella Hassanein

Authors
  1. Doaa Mohey El-Din
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aboul Ella Hassanein
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ehab E. Hassanien
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Doaa Mohey El-Din .

Editor information

Editors and Affiliations

  1. Information Technology Department, Cairo University, Faculty of Computers and Information, Giza, Egypt

    Aboul-Ella Hassanien

  2. Faculty of Computers and Information, Benha University, Banha, Egypt

    Ahmad Taher Azar

  3. Faculty of Computers and Information, Suez Canal University, Ismailia, Egypt

    Tarek Gaber

  4. Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Guadajalara, Jalisco, Mexico

    Diego Oliva

  5. Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

    Fahmy M. Tolba

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

El-Din, D.M., Hassanein, A.E., Hassanien, E.E. (2020). An Automatic Detection of Military Objects and Terrorism Classification System Based on Deep Transfer Learning. In: Hassanien, AE., Azar, A., Gaber, T., Oliva, D., Tolba, F. (eds) Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020). AICV 2020. Advances in Intelligent Systems and Computing, vol 1153. Springer, Cham. https://doi.org/10.1007/978-3-030-44289-7_56

Download citation

Publish with us

Policies and ethics

Search

Navigation