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arXiv · 2506.00154

Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches

Agustín Roca·Gastón Castro·Gabriel Torre·Leonardo J. Colombo·Ignacio Mas·Javier Pereira·Juan I. Giribet
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Abstract

This study compares the performance of state-of-the-art neural networks including variants of the YOLOv11 and RT-DETR models for detecting marsh deer in UAV imagery, in scenarios where specimens occupy a very small portion of the image and are occluded by vegetation. We extend previous analysis adding precise segmentation masks for our datasets enabling a fine-grained training of a YOLO model with a segmentation head included. Experimental results show the effectiveness of incorporating the segmentation head achieving superior detection performance. This work contributes valuable insights for improving UAV-based wildlife monitoring and conservation strategies through scalable and accurate AI-driven detection systems.

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Agustín Roca, Gastón Castro, Gabriel Torre, Leonardo J. Colombo, Ignacio Mas, Javier Pereira, Juan I. Giribet. 2025-05-30. Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches. https://doi.org/10.1109/icuas65942.2025.11007886

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