用无人机立体视觉自动定位松树需修剪枝条，低成本方案可行
Positioning radiata pine branches requiring pruning by drone stereo vision
- 通过对比多种模型在71对图像上分割枝条，选优处理
- 深度学习测距比传统方法更连贯，1-2米内精度高
- 适合想做林业自动化、无人机视觉应用的人参考
本文提出一种基于立体视觉的无人机系统，用于检测并定位辐射松枝条以支持自主修剪。该流程分为两个阶段：枝条分割与深度估计。分割阶段在自建的71对立体图像数据集上，对比了YOLOv8、YOLOv9和Mask R-CNN等模型表现；深度估计阶段则评估了传统方法（SGBM+WLS滤波）及深度学习方法（PSMNet、ACVNet、GWCNet、MobileStereoNet、RAFT-Stereo、NeRF-Supervised Deep Stereo）。提出一种基于质心的三角化算法，并结合中位数绝对偏差（MAD）剔除异常值，计算枝条距离。在1-2米距离下的定性评估显示，深度学习生成的视差图具有更连贯的深度估计效果，验证了低成本立体视觉在林业自动化枝条定位中的可行性。
原文摘要 · Abstract (English)
This paper presents a stereo-vision-based system mounted on a drone for detecting and localising radiata pine branches to support autonomous pruning. The proposed pipeline comprises two stages: branch segmentation and depth estimation. For segmentation, YOLOv8, YOLOv9, and Mask R-CNN variants are compared on a custom dataset of 71 stereo image pairs captured with a ZED Mini camera. For depth estimation, both a traditional method (SGBM with WLS filtering) and deep-learning-based methods (PSMNet, ACVNet, GWCNet, MobileStereoNet, RAFT-Stereo, and NeRF-Supervised Deep Stereo) are evaluated. A centroid-based triangulation algorithm with MAD outlier rejection is proposed to compute branch distance from the segmentation mask and disparity map. Qualitative evaluation at distances of 1-2 m indicates that the deep learning-based disparity maps produce more coherent depth estimates than SGBM, demonstrating the feasibility of low-cost stereo vision for automated branch positioning in forestry.
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