用无人机+双目视觉实现松树枝条精准检测与测距，提升林业修剪安全效率
Drone Stereo Vision for Radiata Pine Branch Detection and Distance Measurement: Integrating SGBM and Segmentation Models
- 融合YOLO检测与SGBM深度估计，实现枝条定位与距离测量
- 实验验证系统可准确识别枝条并测量与无人机的距离
- 为林业自动化提供可复制的技术框架，适合智能农业研究者参考
人工修剪辐射松因树木高大、地形复杂存在重大安全风险。本研究提出基于无人机的修剪系统，配备专用修剪工具与双目视觉相机，实现枝条精准检测与剪裁。采用YOLO和Mask R-CNN等深度学习算法确保枝条检测精度，结合半全局匹配（SGBM）算法实现可靠距离估计。二者协同使系统能精确识别枝条位置，并实现高效定点修剪。实验结果表明，YOLO与SGBM联合应用可准确检测枝条并测量其与无人机的距离。该研究不仅提升了修剪作业的安全性与效率，还推动了无人机在农业与林业自动化中的应用发展，为环境管理智能化提供了基础技术框架。
原文摘要 · Abstract (English)
Manual pruning of radiata pine trees presents significant safety risks due to their substantial height and the challenging terrains in which they thrive. To address these risks, this research proposes the development of a drone-based pruning system equipped with specialized pruning tools and a stereo vision camera, enabling precise detection and trimming of branches. Deep learning algorithms, including YOLO and Mask R-CNN, are employed to ensure accurate branch detection, while the Semi-Global Matching algorithm is integrated to provide reliable distance estimation. The synergy between these techniques facilitates the precise identification of branch locations and enables efficient, targeted pruning. Experimental results demonstrate that the combined implementation of YOLO and SGBM enables the drone to accurately detect branches and measure their distances from the drone. This research not only improves the safety and efficiency of pruning operations but also makes a significant contribution to the advancement of drone technology in the automation of agricultural and forestry practices, laying a foundational framework for further innovations in environmental management.
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