Identification of Military Vehicles Using Vision Systems and AI Methods
Piotr CHOCHÓŁ, Piotr WITKOWSKI, Wojciech ŁAKOMIEC, Norbert KOT, Jakub GÓRSKI, Michał KLUZEK Paweł A. ŁASKI
Abstract. This paper presents a method for identifying graphical equivalents of military vehicles. A YOLO11L neural network model, an enlarged version of the YOLO11 network, was used. YOLO models are algorithms that use the RGB color palette for object detection. LabelStudio software was used to train the neural network for image labeling. To properly train the artificial neural network for vehicle detection, approximately 3,000 images were used [1-3], each containing over 12,000 silhouettes of allied Polish/American and enemy Russian military vehicle models. The model was trained at various standard resolutions: 640×640 and 1024×1024. Because the computing power available on personal computers was insufficient for model training and time was a key factor, Google servers on the Google Colab platform were utilized, leveraging A100 GPUs. Achieved around 7-15 fps for object detection using laptops with RTX 3060 graphics cards. Further research is planned on modifying the network models and training the final network model [3-5].
Keywords
Military Vehicles, Neural Network Model, Identifying Graphical
Published online 1/25/2026, 9 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Piotr CHOCHÓŁ, Piotr WITKOWSKI, Wojciech ŁAKOMIEC, Norbert KOT, Jakub GÓRSKI, Michał KLUZEK Paweł A. ŁASKI, Identification of Military Vehicles Using Vision Systems and AI Methods, Materials Research Proceedings, Vol. 62, pp 253-261, 2026
DOI: https://doi.org/10.21741/9781644904015-33
The article was published as article 33 of the book Terotechnology XIV
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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