A COMPUTER VISION APPROACH TO MILITARY OBJECT RECOGNITION

Authors

  • Krasimir Slavyanov Department Computer Systems and Technologies, Vasil Levski National Military University, Shumen, Bulgaria https://orcid.org/0000-0002-7234-5820

DOI:

https://doi.org/10.68302/std2026.vol3.154

Keywords:

computer vision, military objects, reference images, speeded-up robust features

Abstract

This research described is focused on computer vision tools implemented for detection of a particular military object (or many objects) in a observed scene, given a reference image(s) of the object(s). An algorithm for detecting a specific object based on finding point correspondences between the reference and the target image is designed. It can detect objects despite a scale change or in-plane rotation. It is also robust to small amount of out-of-plane rotation and occlusion. Reference images and a dataset with images obtained from a ground camera are used for the experiments. Detection of point features in images and extraction of feature points are designed for Localization of the recognized object(s) in a scene using matches of feature points (groups of points).

Supporting Agencies

This work was supported by the National Science Program "Security and Defence", which has received funding from the Ministry of Education and Science of the Republic of Bulgaria under the grant agreement no. Д01-74/19.05.2022. The material reflects only the author's opinion, and the Ministry of Education and Science is not responsible for the content.

Downloads

Download data is not yet available.

References

[1] H.Namdari, D.Nanda, X.Yuan, (2022). Progress in Computer Vision: Object Recognition. In: Albert, M.V., Lin, L., Spector, M.J., Dunn, L.S. (eds) Bridging Human Intelligence and Artificial Intelligence. Educational Communications and Technology: Issues and Innovations. Springer, Cham. https://doi.org/10.1007/978-3-030-84729-6_5

[2] M. Vashisht and B. Kumar, "A Survey Paper on Object Detection Methods in Image Processing," 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA), Gunupur, India, 2020, pp. 1-4,

https://doi.org/10.1109/ICCSEA49143.2020.9132871.

[3] T. A. Nayeem, S. M. Motaharuzzaman, A. T. Hoque and M. H. Rahman, "Computer Vision Based Object Detection and Recognition System for Image Searching," 2022 12th International Conference on Electrical and Computer Engineering (ICECE), Dhaka, Bangladesh, 2022, pp. 148-151, https://doi.org/10.1109/ICECE57408.2022.10089019

[4] I. Atik, "Deep Learning In Military Object Detection: An Example of The Yolo-Nas Model," 2024 8th International Symposium on Innovative Approaches in Smart Technologies (ISAS), İstanbul, Turkiye, 2024, pp. 1-7,

https://doi.org/10.1109/ISAS64331.2024.10845459.

[5] J. Cherapanamjeri and B. N. K. Rao, "Neural Networks based Object Detection Techniques in Computer Vision," 2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, 2022, pp. 1092-1099,

https://doi.org/10.1109/ICIRCA54612.2022.9985581.

[6] S. Kaushik, A. Raman and K. V. S. Rajeswara Rao, "Leveraging Computer Vision for Emergency Vehicle Detection-Implementation and Analysis," 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kharagpur, India, 2020, pp. 1-6,

https://doi.org/10.1109/ICCCNT49239.2020.9225331

[7] S. Singh and R. G N, "Military Based Object Detection in Satellite Imagery by Optimising YOLOv8," 2024 IEEE Space, Aerospace and Defence Conference (SPACE), Bangalore, India, 2024, pp. 165-168, https://doi.org/10.1109/SPACE63117.2024.10667819

[8] S. Ali, Abdullah, A. Athar, M. Ali, A. Hussain and H. -C. Kim, "Computer Vision-Based Military Tank Recognition Using Object Detection Technique: An application of the YOLO Framework," 2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC), Jeddah, Saudi Arabia, 2023, pp. 1-6, https://doi.org/10.1109/ICAISC56366.2023.10085552

[9] V. Atanasov, T. Trifonov, “A new model of ultra wideband sensors based interactive system”, International Journal on Information Technologies and Security, № 1 (vol. 16), 2024, pp.39-48, ISSN 1313-8251, https://doi.org/10.59035/XYCG3094

[10] V. Stoyanova, I.Lilov “Steganographic algorithm using the different color components of a 24-bit image”, Security & Future, WEB ISSN 2535-082X; PRINT ISSN 2535-0668, Vol. 5 (2021), Issue 2, pg(s) 61-63

[11] https://www.mathworks.com/help/matlab/index.html [Accessed: Apr. 05, 2026].

Downloads

Published

17.09.2026

Issue

Vol. 3 (2026): Proceedings of the International Conference on Secure Systems Design and Technology Development.

Section

INFORMATION TECHNOLOGY

License

Copyright (c) 2026 Krasimir Slavyanov

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
K. Slavyanov, “A COMPUTER VISION APPROACH TO MILITARY OBJECT RECOGNITION”, SysTechDev, vol. 3, pp. 297–302, Sep. 2026, doi: 10.68302/std2026.vol3.154.