用原始无人机影像提升棕榈树定位精度，更适合野外部署。
From Orthomosaics to Raw UAV Imagery: Enhancing Palm Detection and Crown-Center Localization
- 直接用原始航拍图替代拼接图像，减少预处理依赖
- 原始图像在真实场景中检测准确率更高，跨域泛化仍稳定
- 标注树冠中心能显著提升定位精度，适合生态研究
精准绘制单株树木分布对生态监测与森林管理至关重要。无人机航拍的正射影像虽广泛应用，但拼接伪影和繁重预处理限制其野外部署。本研究探索使用原始无人机影像进行热带雨林中棕榈树检测与树冠中心定位。重点解决两个问题：(1) 检测性能在正射影像与原始影像间的差异，包括同域与跨域迁移；(2) 树冠中心标注是否能提升定位精度。采用先进检测器与关键点模型，结果表明原始影像在实际部署场景下表现更优，而正射影像仍具强跨域泛化能力。训练时引入树冠中心标注可进一步提升定位精度，为下游生态分析提供精确树位信息。研究为基于无人机的生物多样性与保护监测提供实用指导。
原文摘要 · Abstract (English)
Accurate mapping of individual trees is essential for ecological monitoring and forest management. Orthomosaic imagery from unmanned aerial vehicles (UAVs) is widely used, but stitching artifacts and heavy preprocessing limit its suitability for field deployment. This study explores the use of raw UAV imagery for palm detection and crown-center localization in tropical forests. Two research questions are addressed: (1) how detection performance varies across orthomosaic and raw imagery, including within-domain and cross-domain transfer, and (2) to what extent crown-center annotations improve localization accuracy beyond bounding-box centroids. Using state-of-the-art detectors and keypoint models, we show that raw imagery yields superior performance in deployment-relevant scenarios, while orthomosaics retain value for robust cross-domain generalization. Incorporating crown-center annotations in training further improves localization and provides precise tree positions for downstream ecological analyses. These findings offer practical guidance for UAV-based biodiversity and conservation monitoring.
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