Skip to main content
SpringerLink
Log in

Comparing Artificial Intelligence Algorithms in Computer Vision: The Weapon Detection Benchmark

  • Conference paper
  • First Online:
Pattern Recognition and Artificial Intelligence (ICPRAI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13363))

Abstract

The following work proposes a benchmark of performances of state of art AI algorithms for the weapons detection. Particularly, it is aimed to test three CNN based models on the task of detecting specific types of weapons. In order to accomplish this goal, four datasets are employed. Additionally, due to the lack of rich amounts of well-structured datasets in these field of research, new labeled data are produced as a new resource to test specific hypotheses about their impact on the performances of the models: different transfer-learning approaches are studied to understand how specific types of data could increase the generalization of the trained algorithms, with a peculiar attention to realistic scenarios. The whole work is designed with the intention to select the state-of-art algorithms that truly could be employed for realistic applications.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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. Debnath, R., Bhowmik, M.K.: A comprehensive survey on computer vision based concepts, methodologies, analysis and applications for automatic gun/knife detection. J. Vis. Commun. Image Represent. 78, 103165 (2021)

    Article  Google Scholar 

  2. Bhatti, M.T., Khan, M.G., Aslam, M., Fiaz, M.J.: Weapon detection in real-time CCTV videos using deep learning. IEEE Access 9, 34366–34382 (2021)

    Article  Google Scholar 

  3. Romero, D., Salamea, C.: Convolutional models for the detection of firearms in surveillance videos. Appl. Sci. 9(15), 2965 (2019)

    Article  Google Scholar 

  4. Olmos, R., Tabik, S., Herrera, F.: Automatic handgun detection alarm in videos using deep learning. Neurocomputing 275, 66–72 (2018)

    Article  Google Scholar 

  5. Castillo, A., Tabik, S., Pérez, F., Olmos, R., Herrera, F.: Brightness guided preprocessing for automatic cold steel weapon detection in surveillance videos with deep learning. Neurocomputing 330, 151–161 (2019)

    Article  Google Scholar 

  6. Elmir, Y., Laouar, S.A., Hamdaoui, L.: Deep learning for automatic detection of handguns in video sequences. In: JERI (2019)

    Google Scholar 

  7. Cardoso, G., Simões, V., Ciarelli, P.M., Vassallo, R.F.: Use of deep learning for firearms detection in images. In: Anais do XV Workshop de Visão Computacional, pp. 109–114. SBC (2019)

    Google Scholar 

  8. Dubey, S.:Building a gun detection model using deep learning. Program Chair & Proceedings Editor: M. Afzal Upal, Ph.d. Chair of Computing & Information Science Department Mercyhurst University 501 (2019)

    Google Scholar 

  9. Fernandez-Carrobles, M.M., Deniz, O., Maroto, F.: Gun and knife detection based on faster R-CNN for video surveillance. In: Morales, A., Fierrez, J., Sánchez, J.S., Ribeiro, B. (eds.) IbPRIA 2019. LNCS, vol. 11868, pp. 441–452. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-31321-0_38

    Chapter  Google Scholar 

  10. de Azevedo Kanehisa, R.F., de Almeida Neto, A.: Firearm detection using convolutional neural networks. In: ICAART 2019, pp. 707–714 (2019)

    Google Scholar 

  11. Warsi, A., Abdullah, M., Husen, M.N., Yahya, M., Khan, S., Jawaid, N.: Gun detection system using YOLOv3. In: 2019 IEEE International Conference on Smart Instrumentation, Measurement and Application (ICSIMA), pp. 1–4. IEEE (2019)

    Google Scholar 

  12. Jain, H., Vikram, A., Kashyap, A., Jain, A.:Weapon detection using artificial intelligence and deep learning for security applications. In: 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), pp. 193–198. IEEE (2020)

    Google Scholar 

  13. Akcay, S., Kundegorski, M.E., Willcocks, C.G., Breckon, T.P.: Using deep convolutional neural network architectures for object classification and detection within x-ray baggage security imagery. IEEE Trans. Inf. Forensics Secur. 13(9), 2203–2215 (2018)

    Article  Google Scholar 

  14. Iqbal, J., Munir, M.A., Mahmood, A., Ali, A.R., Ali, M.:Leveraging orientation for weakly supervised object detection with application to firearm localization. arXiv preprint arXiv:1904.10032 (2019)

  15. Huang, J., et al.: Speed/accuracy trade-offs for modern convolutional object detectors. In: CVPR 2017 (2017)

    Google Scholar 

  16. Goldsborough, P.: A tour of tensorflow. arXiv preprint arXiv:1610.01178 (2016)

  17. Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)

  18. Guns used in crime, Washington, DC: US Department of Justice: Bureau of Justice Statistics Selected Findings, publication NCJ-148201 (1995)

    Google Scholar 

  19. World Health Organization: European report on preventing violence and knife crime among young people. World Health Organization. Regional Office for Europe (2010)

    Google Scholar 

  20. Kuznetsova, A., et al.: The open images dataset v4: unified image classification, object detection, and visual relationship detection at scale. arXiv preprint arXiv:1811.00982 (2018)

  21. Pérez-Hernández, F., Tabik, S., Lamas, A., Olmos, R., Fujita, H., Herrera, F.: Object detection binary classifiers methodology based on deep learning to identify small objects handled similarly: application in video surveillance. Knowl. Based Syst. 194, 105590 (2020)

    Article  Google Scholar 

  22. Everingham, M., Winn, J.: The pascal visual object classes challenge 2012 (voc2012) development kit. Pattern Analysis, Statistical Modelling and Computational Learning, Technical report, 8, 5 (2011)

    Google Scholar 

  23. Lin, T.-Y., et al.: Microsoft coco: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) Computer Vision – ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6–12, 2014, Proceedings, Part V, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    Chapter  Google Scholar 

  24. Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y.M.:Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)

  25. Mahsereci, M., Balles, L., Lassner, C., Hennig, P.: Early stopping without a validation set. arXiv preprint arXiv:1703.09580 (2017)

  26. Prechelt, L.: Early stopping - but when? In: Orr, G.B., Müller, K.-R. (eds.) Neural Networks: Tricks of the trade, pp. 55–69. Springer, Heidelberg (1998). https://doi.org/10.1007/3-540-49430-8_3

    Chapter  Google Scholar 

  27. Shetty, S.: Application of convolutional neural network for image classification on Pascal VOC challenge 2012 dataset. arXiv preprint arXiv:1607.03785 (2016)

Download references

Author information

Authors and Affiliations

  1. University of Bari “Aldo Moro”, Bari, Italy

    Vincenzo Dentamaro, Paolo Giglio, Donato Impedovo & Giuseppe Pirlo

Authors
  1. Vincenzo Dentamaro
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paolo Giglio
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Donato Impedovo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Giuseppe Pirlo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Paolo Giglio .

Editor information

Editors and Affiliations

  1. Télécom SudParis, Palaiseau, France

    Mounîm El Yacoubi

  2. École de Technologie Supérieure, Montreal, QC, Canada

    Eric Granger

  3. Hong Kong Baptist University, Kowloon, Kowloon, Hong Kong

    Pong Chi Yuen

  4. Indian Statistical Institute, Kolkata, India

    Umapada Pal

  5. Université Paris Cité, Paris, France

    Nicole Vincent

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dentamaro, V., Giglio, P., Impedovo, D., Pirlo, G. (2022). Comparing Artificial Intelligence Algorithms in Computer Vision: The Weapon Detection Benchmark. In: El Yacoubi, M., Granger, E., Yuen, P.C., Pal, U., Vincent, N. (eds) Pattern Recognition and Artificial Intelligence. ICPRAI 2022. Lecture Notes in Computer Science, vol 13363. Springer, Cham. https://doi.org/10.1007/978-3-031-09037-0_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-09037-0_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-09036-3

  • Online ISBN: 978-3-031-09037-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation