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Integrating deep learning-based object detection
models with unmanned aerial vehicles (UAVs) enables faster,
more efficient, and cost-effective livestock monitoring. However,
deep learning models require large and diverse datasets to achieve
high accuracy. Traditional data augmentation techniques may be
inadequate for complex tasks like object detection. Therefore, this
study evaluates the performance of deep learning models on goat
and cattle images using Cutout, CutMix, MixUp, and Mosaic
data augmentation techniques. Ablation experiments revealed
that Mosaic augmentation contributed the most to model success.
These findings highlight the critical role of selecting the right data
augmentation strategy in enhancing the stability and scalability
of UAV-based livestock analysis.
International Conference on Advanced Technologies, Computer Engineering and Science
ICATCES
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Yusuf Yargı BAYDILLI