用摄像头实现松树修剪的自动枝干检测与测距，安全又省钱。
YOLO and SGBM Integration for Autonomous Tree Branch Detection and Depth Estimation in Radiata Pine Pruning Applications
- YOLO+立体视觉融合，仅靠双目相机实现枝干定位
- 枝干分割mAPmask50-95达82.0%，单帧处理时间<1秒
- 适合需要低成本自动化作业的林业场景
人工修剪辐射松因工作高度大、地形复杂，存在显著安全风险。本文提出一种计算机视觉框架，将YOLO目标检测与半全局块匹配（SGBM）立体视觉相结合，实现基于无人机的自主修剪。系统仅使用双目相机输入，即可精准检测枝干并估计深度，无需昂贵的激光雷达。实验表明，YOLO在枝干分割上优于Mask R-CNN，mAPmask50-95达到82.0%。集成系统在2米作业范围内精确定位枝干，每帧处理时间低于1秒。结果验证了低成本自主修剪系统的可行性，可提升商业林业中的作业安全与效率。
原文摘要 · Abstract (English)
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.
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