Recognition of military equipment using computer vision methods

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dc.contributor.advisorСалата, Кирилоuk_UA
dc.contributor.authorАнтощук, Романuk_UA
dc.date.accessioned2025-09-09T06:54:56Z
dc.date.available2025-09-09T06:54:56Z
dc.date.issued2025
dc.description.abstractThis research explores how to achieve the minimum sufficient performance for military equipment recognition within a hypothetical, wide-area situationalawareness system. Once demanded deep expertise in neural-network architecture design and complex deployment pipelines can now be realized using two YOLOv8 models built entirely on open-source datasets, frameworks, and free cloud GPUs delivering 69.5% and 72.4% mAP@50 accuracy. We met the problem of merging sources with wildly varying scopes, resolving labeling inconsistencies and duplicate images that inflated confidence scores, addressing severe class imbalance across 14 vehicle categories, and witnessing inter-class confusion among visually similar targets. This work demonstrates that, with modern tools, building a robust military equipment reconnaissance pipeline - from raw images to deployable video inference is both practical and accessible, achieving high accuracy on fine-grained classes while running smoothly on limited GPU resources.en_US
dc.identifier.urihttps://ekmair.ukma.edu.ua/handle/123456789/36519
dc.language.isoen_USen_US
dc.statusfirst publisheden_US
dc.subjectrecognitionen_US
dc.subjectmilitary equipmenten_US
dc.subjectneural-networken_US
dc.subjectcomplex deployment pipelinesen_US
dc.subjectterm paperen_US
dc.titleRecognition of military equipment using computer vision methodsen_US
dc.title.alternativeРозпізнавання військової техніки на фото і відео за допомогою технологій комп’ютерного зоруuk_UA
dc.typeOtheren_US
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