Analysis of Object Identification Methods for FPV Drones

Chebanchyk, D.; Yevsieiv, V.

Публікація:
Analysis of Object Identification Methods for FPV Drones

dc.contributor.author	Chebanchyk, D.
dc.contributor.author	Yevsieiv, V.
dc.date.accessioned	2025-10-03T20:53:24Z
dc.date.available	2025-10-03T20:53:24Z
dc.date.issued	2025
dc.description.abstract	The abstracts of the report consider modern methods of object identification for FPV drones with an emphasis on their application in real time and under conditions of limited computing resources. Classical approaches based on keypoint extraction, deep convolutional neural networks, semantic and instance-segmentation methods, as well as state filters and lightweight optimized models are analyzed. The study shows that each of the methods has its advantages and limitations depending on the accuracy, processing speed and complexity of the environment. Special attention is paid to hybrid approaches that combine the advantages of several methods to ensure stable and effective object identification on board FPV drones. The results obtained emphasize the need to optimize algorithms and adapt models to the resource constraints of drones to ensure reliability and accuracy of operation in dynamic conditions.
dc.identifier.citation	Chebanchyk D. Analysis of Object Identification Methods for FPV Drones / D. Chebanchyk, V. Yevsieiv // Manufacturing & Mechatronic Systems 2025 : Thesises of Reports of IX-st International Conference, October 25-26, 2025. - Kharkiv, 2025. - P. 30-33.
dc.identifier.uri	https://openarchive.nure.ua/handle/document/32847
dc.language.iso	en_US
dc.subject	FPV drones
dc.subject	object identification
dc.subject	deep neural networks, segmentation
dc.subject	Kalman filter
dc.subject	lightweight models
dc.title	Analysis of Object Identification Methods for FPV Drones
dc.type	Thesis
dspace.entity.type	Publication