用无人机+双目视觉+YOLO实现松树枝条精准检测与测距
Drone Stereo Vision for Radiata Pine Branch Detection and Distance Measurement: Utilizing Deep Learning and YOLO Integration
- 结合YOLO与双目视觉，实现枝条分割与深度估计
- 深度学习生成的深度图比SGBM更精确，误差更低
- 适用于智能修剪系统，提升农业自动化水平
本研究开发了一种配备修剪工具和双目视觉相机的无人机，用于精准检测和测量树木枝条的空间位置。采用YOLO进行枝条分割，对比单目与双目两种深度估计方法。在缺乏真实标注数据的情况下，通过深度神经网络微调以逼近最优深度值。实验结果表明，基于深度学习的深度图生成方法相较于SGBM更具精度与细节表现，显著提升了分支检测与距离测量的准确性和效率，为修剪作业的自动化提供了有效技术路径，展现了深度学习在农业智能化中的应用潜力。
原文摘要 · Abstract (English)
This research focuses on the development of a drone equipped with pruning tools and a stereo vision camera to accurately detect and measure the spatial positions of tree branches. YOLO is employed for branch segmentation, while two depth estimation approaches, monocular and stereo, are investigated. In comparison to SGBM, deep learning techniques produce more refined and accurate depth maps. In the absence of ground-truth data, a fine-tuning process using deep neural networks is applied to approximate optimal depth values. This methodology facilitates precise branch detection and distance measurement, addressing critical challenges in the automation of pruning operations. The results demonstrate notable advancements in both accuracy and efficiency, underscoring the potential of deep learning to drive innovation and enhance automation in the agricultural sector.
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