Abstract
Manual pruning of radiata pine trees poses significant safety risks due to extreme working heights and challenging terrain. This paper presents a computer vision framework that integrates YOLO object detection with Semi-Global Block Matching (SGBM) stereo vision for autonomous drone-based pruning operations. Our system achieves precise branch detection and depth estimation using only stereo camera input, eliminating the need for expensive LiDAR sensors. Experimental evaluation demonstrates YOLO's superior performance over Mask R-CNN, achieving 82.0% mAPmask50-95 for branch segmentation. The integrated system accurately localizes branches within a 2 m operational range, with processing times under one second per frame. These results establish the feasibility of cost-effective autonomous pruning systems that enhance worker safety and operational efficiency in commercial forestry.
Abstract (translated)
手动修剪辐射松树由于极端的工作高度和复杂的地形，存在显著的安全风险。本文提出了一种计算机视觉框架，该框架结合了YOLO目标检测技术和半全局块匹配（SGBM）立体视觉技术，用于基于自主无人机的修剪作业。我们的系统仅通过使用双目摄像头输入实现了精确的枝条检测和深度估计，从而无需昂贵的激光雷达传感器。 实验评估表明，在枝条分割方面，YOLO的表现优于Mask R-CNN，达到82.0% mAPmask50-95。集成系统能够在一个2米的操作范围内准确定位枝条，并且处理时间每帧不到一秒。这些结果证明了低成本自主修剪系统的可行性，该系统可以提高工人的安全性和商业林业的运营效率。
URL
https://arxiv.org/abs/2512.05412
