利用无人机立体视觉定位需修剪的辐射松枝位置
Positioning radiata pine branches requiring pruning by drone stereo vision
AI总结：
本文提出基于无人机的立体视觉系统，用于检测和定位辐射松枝以支持自主修剪。通过比较不同模型在自定义数据集上的表现，验证了深度学习方法在低成本立体视觉中用于林业自动枝条定位的可行性。
摘要：
本文提出了一种基于无人机的立体视觉系统，用于检测和定位辐射松枝以支持自主修剪。所提出的流程分为两个阶段：枝条分割和深度估计。在分割阶段，YOLOv8、YOLOv9和Mask R-CNN变体在71对自定义立体图像对上进行了比较，这些图像对由ZED Mini相机拍摄。在深度估计阶段，传统方法（SGBM结合WLS滤波）和基于深度学习的方法（PSMNet、ACVNet、GWCNet、MobileStereoNet、RAFT-Stereo和NeRF-Supervised Deep Stereo）均被评估。提出了一种基于质心的三角化算法，结合MAD异常值剔除，用于计算从分割掩码和视差图到枝条的距离。在1-2米距离的定性评估表明，基于深度学习的视差图产生的深度估计比SGBM更一致，证明了低成本立体视觉在林业自动枝条定位中的可行性。
英文摘要：
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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