结合目标检测与几何感知聚类以区分无人机影像中的重叠植株
Combining Object Detection with Geometry-Aware Clustering to Distinguish Overlapping Plants in UAV Imagery
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中文总结 AI 辅助
本研究提出一种几何感知后检测框架，结合目标检测与聚类，利用RGB无人机影像区分重叠植株，在茄子与番茄作物上验证了有效性，无需额外传感器或重训练。
中文摘要 AI 辅助
从无人机影像中获取可靠的植株级信息对于自动化作物监测至关重要。然而，在密集的作物冠层中，相邻植株经常发生重叠并被检测为单一目标，从而降低了植株级测量的可靠性。本研究提出了一种基于几何感知的后检测框架，利用标准RGB无人机影像来解析重叠的植株实例。该框架将目标检测与植株组成部分的几何聚类相结合。在每个灌木级区域内检测到的叶片或枝条通过两种互补的几何特征来表示：组成部分的质心和由检测到的植株结构导出的径向交点。K均值和高斯混合模型用于确定检测区域包含单株还是两株重叠植株。密度滤波抑制了虚假的径向交点，后处理集成结合了空间和方向几何信息。该框架在田间条件下使用茄子和番茄作物的无人机影像进行了评估。基于质心的聚类在茄子作物上实现了0.89的F1分数，而质心与径向交点组合的方法在番茄作物上取得了最佳性能，使用K均值时准确率为0.80，精确率为1.00，F1分数为0.75。密度滤波显著改善了番茄作物中基于径向交点的聚类。所提出的方法提供了一种轻量级、模块化的工程解决方案，无需额外的深度传感器、像素级分割、三维重建或对主要灌木检测器的重新训练，即可集成到现有的RGB无人机监测流程中。结果表明，对现有检测器输出进行几何推理可以补充基于深度学习的物体检测，并改善密集农业冠层中的植株级解释。
英文摘要
Reliable plant-level information from unmanned aerial vehicle (UAV) imagery is important for automated crop monitoring. However, in dense crop canopies, adjacent plants frequently overlap and are detected as a single object, reducing the reliability of plant-level measurements. This study presents a geometry-aware post-detection framework for resolving overlapping plant instances using standard RGB UAV imagery. The framework combines object detection with geometric clustering of plant components. Leaves or branches detected within each bush-level region are represented using two complementary geometric features: component centroids and radial intersection points (RIPs) derived from detected plant structures. K-means and Gaussian mixture models determine whether a detected region contains a single plant or two overlapping plants. Density filtering suppresses spurious radial intersections, and a post-pipeline ensemble combines spatial and directional geometric information. The framework was evaluated using UAV imagery of eggplant and tomato crops under field conditions. Centroid-based clustering achieved an F1-score of 0.89 for eggplant, while the combined centroid-RIP approach achieved the best tomato performance, with an accuracy of 0.80, precision of 1.00, and F1-score of 0.75 using K-means. Density filtering substantially improved RIP-based clustering for tomato. The proposed approach provides a lightweight, modular engineering solution that can be integrated with existing RGB UAV monitoring pipelines without additional depth sensors, pixel-level segmentation, three-dimensional reconstruction, or retraining of the primary bush detector. The results demonstrate that geometric reasoning applied to existing detector outputs can complement deep-learning-based object detection and improve plant-level interpretation in dense agricultural canopies.
发表机构
- Rowan University（罗文大学）
机构由 AI 辅助整理，请以论文原文为准。
