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Bakirci et al., 2024 - Google Patents

Multi-Class Vehicle Detection and Classification with YOLO11 on UAV-Captured Aerial Imagery

Bakirci et al., 2024

Document ID
1581290724881728188
Author
Bakirci M
Dmytrovych P
Bayraktar I
Anatoliyovych O
Publication year
Publication venue
2024 IEEE 7th International Conference on Actual Problems of Unmanned Aerial Vehicles Development (APUAVD)

External Links

Snippet

Aerial imaging and object detection using unmanned aerial vehicle (UAV) systems pose unique challenges, including varying altitudes, dynamic backgrounds, and changes in lighting and weather conditions. These factors complicate the detection process, demanding …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

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    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/68Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
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    • G06K9/6288Fusion techniques, i.e. combining data from various sources, e.g. sensor fusion
    • GPHYSICS
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    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00624Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G06K9/00771Recognising scenes under surveillance, e.g. with Markovian modelling of scene activity
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