标题： 使用无人机图像检测濒危鹿种：高效深度学习方法之间的对比研究 显示英文标题

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

摘要： 本研究比较了最先进的神经网络的性能，包括 YOLOv11 和 RT-DETR 模型的各种变体，用于检测无人机图像中的沼泽鹿，在样本占据图像非常小的部分且被植被遮挡的场景中。 我们扩展了先前的分析，为数据集添加了精确的分割掩模，从而实现了包含分割头的 YOLO 模型的细粒度训练。 实验结果表明，加入分割头的有效性，实现了卓越的检测性能。 本研究通过可扩展且准确的 AI 驱动检测系统，为改善基于无人机的野生动物监测和保护策略提供了有价值的见解。 显示英文摘要

摘要： 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.