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

• URL: http://arxiv.org/abs/2506.00154v1
• Date: Fri, 30 May 2025 18:45:42 GMT
• Title: Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches
• Authors: Agustín Roca, Gastón Castro, Gabriel Torre, Leonardo J. Colombo, Ignacio Mas, Javier Pereira, Juan I. Giribet,
• Abstract summary: This study compares the performance of state-of-the-art neural networks for detecting marsh deer in UAV imagery.<n>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.
• Score: 1.130790932059036
• License: http://creativecommons.org/licenses/by/4.0/
• 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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