无人机自动导航抓拍动物侧身，提升个体识别准确率。
Autonomous UAV Navigation for Individual Wildlife Re-Identification
- 用目标检测+姿态分类指导飞行，主动对准动物侧身
- 在肯尼亚实测中成功识别斑马，泛化至长颈鹿等物种
- 为生态监测设计任务感知的智能飞行系统，适合保护研究
可靠的野生动物个体重识别对种群监测、行为追踪和保护政策评估至关重要，但大规模数据采集仍依赖生态学家或公民科学家的大量人工工作。本文提出一种自主无人机导航系统，主动优化图像采集以支持下游重识别任务，突破传统被动航拍局限。系统结合YOLOv11目标检测与基于DINOv2的姿态分类器，实时决策飞行：定位动物、调整角度使其侧身朝向相机（用于花纹识别的关键区域），并在目标边界框达到最小阈值时完成接近。与以往侧重群体行为视频采集的无人机系统不同，本方法专为个体识别模型的图像质量需求定制。通过肯尼亚斑马的案例研究验证可行性，并证明该方法可推广至具有诊断性表面特征的其他物种，包括长颈鹿、老虎和大象。本工作建立了一个任务感知的具身人工智能框架，使下游重识别需求驱动实时感知与控制。
原文摘要 · Abstract (English)
Reliable individual re-identification (re-ID) of wildlife is essential for population monitoring, behavioral tracking, and conservation policy evaluation, yet large-scale data collection remains labor-intensive, relying on manual efforts by ecologists or citizen scientists. We propose an autonomous drone navigation system that actively optimizes image capture for downstream re-ID, moving beyond passive aerial sensing. The system combines YOLOv11 object detection with a DINOv2-based pose classifier to guide real-time flight decisions: detecting animals, orienting to expose the lateral flank (the surface of interest for pattern-based re-ID), and approaching until the subject meets a minimum bounding-box threshold. Unlike prior drone systems that optimize for group-level behavioral video, ours targets the specific image-quality requirements of individual-identification models. We demonstrate feasibility through a case study on zebra using footage collected in Kenya, and show the approach generalizes to other species with diagnostic surface patterns, including giraffes, tigers, and elephants. Our work establishes a framework for task-aware embodied AI for ecological data collection, in which downstream re-ID requirements drive real-time perception and control.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。