构建多环境多物种低空无人机数据集，提升野生动物检测泛化能力
MMLA: Multi-Environment, Multi-Species, Low-Altitude Drone Dataset
- 跨三地采集37段高清视频，覆盖六种动物的低空飞行影像
- 在新数据集上微调YOLOv11m，mAP50达82%，较基线提升52个百分点
- 为自动驾驶无人机野外监测提供高多样性训练数据，适合生态研究者
实时无人机影像中的野生动物检测对生态保护与监测至关重要。然而，标准检测模型如YOLO在不同地点间泛化能力差，且难以识别稀有物种，限制了其在自动无人机部署中的应用。本文提出MMLA，一个全新的多环境、多物种、低空无人机数据集，覆盖肯尼亚的奥尔佩贾特保护地和姆帕拉研究中心，以及俄亥俄州的《野地》自然保护区，包含六种动物（斑马、长颈鹿、瞪羚、非洲野犬等）。数据集涵盖37段高分辨率视频，共81.1万标注。基线YOLO模型在不同地点表现差异显著；而将YOLOv11m在MMLA上微调后，mAP50达到82%，相比基线提升52个百分点。结果表明，多样化的训练数据对实现自主无人机系统中稳健的动物检测至关重要。
原文摘要 · Abstract (English)
Real-time wildlife detection in drone imagery supports critical ecological and conservation monitoring. However, standard detection models like YOLO often fail to generalize across locations and struggle with rare species, limiting their use in automated drone deployments. We present MMLA, a novel multi-environment, multi-species, low-altitude drone dataset collected across three sites (Ol Pejeta Conservancy and Mpala Research Centre in Kenya, and The Wilds in Ohio), featuring six species (zebras, giraffes, onagers, and African wild dogs). The dataset contains 811K annotations from 37 high-resolution videos. Baseline YOLO models show performance disparities across locations while fine-tuning YOLOv11m on MMLA improves mAP50 to 82%, a 52-point gain over baseline. Our results underscore the need for diverse training data to enable robust animal detection in autonomous drone systems.
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