发表机构
Aalborg University; University of Applied Sciences Upper Austria; University of Novi Sad; Massachusetts Institute of Technology（奥尔堡大学; 上奥地利应用科学大学; 诺威萨德大学; 麻省理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究以马鹿为对象，融合航拍RGB与热红外视频的自监督DINOv3特征，构建多模态分类流程，在四次飞行中实现26只个体25只的正确分类，提升了种群性别与生命阶段分类的鲁棒性，可自动化获取兽群结构数据。
AI 中文摘要
无人机航拍调查越来越多地支持野生动物种群数量估算，但有用的普查不止是计数：种群动态由物种组成、性别比例和年龄结构定义，即存在哪些物种，以及兽群如何分为成年雄性、成年雌性和幼体。我们以马鹿（*Cervus elaphus*）为测试案例，因为管理者会依据这些动态采取行动，且定义成年雄性的可见特征——鹿角具有季节性变化。调查采用天底视角飞行，高度足够高以不干扰动物，因此每只鹿仅占据小而低分辨率的斑块。两种记录模态在相反条件下失效：在可见光模态中，树冠下的鹿会与地面融为一体；而在热红外模态中，鹿会成为丢失细节的亮斑。我们不单独信任任一模态，而是在每个阶段使用自监督DINOv3特征对它们进行融合。我们的流程在两种模态中追踪动物，仅当两台相机达成一致时才确认动物，仅保留清晰、无遮挡的帧，并通过对这些帧投票来分配物种和性别；生命阶段则通过地理参考的体型单独判断，因为在调查分辨率下，幼体通常仅在体型上与成年雌性不同。在跨越鹿角季节的四次飞行中，融合流程正确分类了26只被检测个体中的25只（8只成年雄性中的7只、全部16只成年雌性、2只幼体），而单独使用任一传感器仅能分类26只中的20只。多模态物种分类准确率达到96.0%，而对于性别分类，融合两种传感器最为关键：结合RGB与热红外的模型在不同环境和季节中最为鲁棒。人口统计分类自动化将无人机飞行从计数转变为兽群结构的可重复读取，因此管理者已在依据的性别比例和年龄结构可在每次调查飞行时收集。
英文摘要
Aerial drone surveys increasingly support wildlife population estimation, yet a useful census is more than a count: population dynamics are defined by species composition, sex ratios and age structure, that is, by which species are present and how a herd splits into adult males, adult females and juveniles. We use red deer ($\textit{Cervus elaphus}$) as a test case, because managers act on these dynamics and because the visible cue defining adult males, the antlers, is seasonally variable. Surveys are flown nadir, high enough not to disturb the animals, so each deer occupies only a small, low-resolution patch. The two recording modalities fail in opposite conditions: in color a deer under canopy blends into the ground, while in thermal it becomes a bright blob that loses fine detail. Rather than trust either modality alone, we fuse them at every stage using self-supervised DINOv3 features. Our pipeline tracks animals in both modalities, treats an animal as confirmed only when the two cameras agree, keeps only the clear, non-occluded frames, and assigns species and sex by a vote across them; life stage is read separately from geo-referenced body size, since at survey resolution a juvenile often only differs from an adult female in size. Across four flights spanning the antler season the fused pipeline correctly classifies 25 of the 26 detected individuals (7 of 8 adult males, all 16 adult females and 2 juveniles), against 20 of 26 for either sensor alone. Multimodal species classification reaches 96.0%, while for sex classification fusing the two sensors matters most: the combined RGB+thermal model is the most robust across environments and seasons. Automating the demographic classification turns a drone flight from a count into a repeatable reading of herd structure, so the sex ratios and age structure that managers already act on can be gathered as often as a survey can be flown.
CommentsAccepted at the ECCV 2026 Workshop on Computer Vision for Ecology (CV4Ecology), archival proceedings track. 17 pages, 7 figures, 5 tables
