Computer Vision for UAV-based Reconnaissance under Conditions of Modern Warfare

Authors

DOI:

https://doi.org/10.18372/1990-5548.86.20560

Keywords:

unmanned aerial vehicle, computer vision, deep learning, object detection, military reconnaissance, neural networks

Abstract

This paper considers the application of computer vision and deep learning methods for automated aerial reconnaissance using unmanned aerial vehicles under the conditions of modern warfare. The main classes of reconnaissance objects are analyzed, including military vehicles, fortifications, artillery positions, and groups of personnel. An approach to building an object detection system based on deep neural networks is proposed, in particular using YOLO-type detectors and U-Net segmentation models. The process of data preparation and augmentation with consideration of combat factors (smoke, explosions, low illumination, image shift, and noise) is described. An experimental evaluation of object detection quality under different scenarios is performed. It is shown that the use of specially adapted augmentation significantly increases the robustness of the models to interference. The limitations of the proposed approach and directions for further research are discussed.

Author Biography

Anatoly Kot , National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute,”

PhD Student

Artificial Intelligence Department

Educational and Research Institute for Applied System Analysis

References

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Published

2025-12-13

How to Cite

Kot , A. (2025). Computer Vision for UAV-based Reconnaissance under Conditions of Modern Warfare. Electronics and Control Systems, 4(86), 39–43. https://doi.org/10.18372/1990-5548.86.20560

Issue

Vol. 4 No. 86 (2025)

Section

COMPUTER SCIENCE

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