用无人机影像检测濒危鹿类，提升小目标与遮挡下的识别精度
Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches
- 融合分割头的YOLO模型增强小目标定位能力
- 在植被遮挡场景下检测准确率显著优于传统方法
- 适合野生动物保护中高精度无人监测系统应用
本研究对比了最新的神经网络模型（包括YOLOv11和RT-DETR变体）在无人机影像中检测沼泽鹿的表现，针对目标占图像比例极小且被植被遮挡的复杂场景。通过扩展数据集并引入精确分割掩码，实现了包含分割头的YOLO模型的细粒度训练。实验结果表明，加入分割头能显著提升检测性能。该工作为基于无人机的野生动物监测与保护策略提供了可扩展、高精度的AI检测解决方案。
原文摘要 · Abstract (English)
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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