Application of computer vision for target detection in multi-level military reconnaissance systems: Literature review

Denys Berkovskyi*, Alina Artomova

d.s.berkovskyi@khai.edu

Received 21.08.2025, Revised 25.02.2026, Accepted 28.04.2026, Published 29.05.2026
Abstract

The purpose of the research was to analyse modern computer vision methods for automatic detection and classification of targets in multi-level military intelligence systems, as well as to evaluate the effectiveness of deep learning algorithms in processing intelligence data under various observation conditions. To achieve this goal, the approach integrated a systematic literature review, the modelling of image processing architectures, and a comparative evaluation of neural network-based object detection models. Findings indicated that the deployment of state-of-the-art deep learning architectures specifically convolutional neural networks, YOLO (You Only Look Once) variants, and transformer-based models substantially improves the accuracy and robustness of automated military object detection in imagery acquired from unmanned aerial vehicles and satellite sources. Moreover, combining data from multiple sensors with multimodal processing techniques enhanced target recognition capabilities, particularly in scenarios characterised by low visibility, complex scene composition, or partial object occlusion. A comparative analysis of modern architectures has shown that the YOLO family models provide the best processing speed and are most suitable for real-time application on board unmanned aerial vehicles, while Faster Region-based Convolutional Neural Network demonstrates higher localisation accuracy for complex and small-sized objects, but requires higher computational costs. Convolutional neural network architectures provide a balanced ratio between accuracy and performance, while transformer-based models work more effectively in difficult observation conditions, in particular, with low spatial resolution, noise, partial overlap and masking of objects, but their use is accompanied by increased requirements for computational resources. In general, a compromise has been established between detection accuracy, processing speed and computational complexity of the models, which determines the feasibility of their application depending on the characteristics of the reconnaissance platform and the conditions for performing the task. Information was provided on the development of intelligent systems for automated analysis of intelligence data

Keywords:
deep learning; computer vision; object detection; multisensory data fusion; real-time analysis; unmanned aerial vehicle

Retrieved from Volume 24, No. 1, 2026

Pages 107–119

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Berkovskyi, D., & Artomova, A. (2026). Application of computer vision for target detection in multi-level military reconnaissance systems: Literature review. Journal of Kryvyi Rih National University, 24(1), 107-119. https://doi.org/10.31721/2306-5451-2026-1-24-107-119
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