CLASSIFICATION OF MILITARY VEHICLES USING CONVOLUTIONAL NEURAL NETWORKS (CNNs)

Authors

  • Bohdan Somriakov Petro Mohyla Black Sea National University
  • Svitlana Borovlova Petro Mohyla Black Sea National University

DOI:

https://doi.org/10.34132/mspc2025.01.14.20

Keywords:

Convolutional Neural Network (CNN), Military Vehicles Classification, Image Recognition, Deep Learning, Image Processing, Machine Learning, Computer Vision.

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.

References

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What is Overfitting? URL: https://aws.amazon.com/what-is/overfitting/#:~:text=Overfitting%20is%20an%20undesirable%20machine,on%20a%20known%20data%20set. (дата звернення 19.05.2025).

Military Applications of Machine Learning: A Bibliometric Perspective. URL: https://www.researchgate.net/publication/360162218_Military_Applications_of_Machine_Learning_A_Bibliometric_Perspective. (дата звернення 19.05.2025).

Published

2025-05-19