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October 10, 2025

CLASSIFICATION OF MILITARY VEHICLES USING CONVOLUTIONAL NEURAL NETWORKS (CNNs)

BSBohdan SomriakovPetro Mohyla Black Sea National University SBSvitlana BorovlovaPetro Mohyla Black Sea National University

Key Points

The study confirms that deep learning techniques effectively classify military vehicles based on image analysis.
A dataset of over 14,000 images across 10 military vehicle categories was utilized to train and test the model.
This approach can enhance military analytics and facilitate drone-based surveillance systems in defense operations.
The findings underscore the growing relevance of automated systems in military and defense research.

Abstract

The thesis presents a convolutional neural network (CNN) model for the automatic classification of military vehicles based on images. The study is based on a dataset of over 14,000 images, divided into training and testing sets, covering 10 categories including tanks, artillery, infantry fighting vehicles, and anti-aircraft systems. The results confirm the effectiveness of deep learning in recognizing military objects. The proposed approach has potential applications in military analytics, drone-based surveillance systems, automated target detection, and defense-related research.

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