Abstract
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.
Abstract (translated)
手动剪枝松树树存在显著的安全风险,因为它们的高度巨大,生长环境具有挑战性。为解决这些风险,这项研究提出了一个采用无人机和专用剪枝工具以及立体视觉摄像头的剪枝系统,实现对树枝的准确检测和修剪。深度学习算法,包括YOLO和Mask R-CNN,用于确保准确分支检测,而半全局匹配算法被集成以提供可靠的距离估计。这些技术之间的协同作用有助于精确确定分支位置,实现有针对性的剪枝。实验结果表明,YOLO和SGBM的联合应用使无人机能够准确检测树枝并测量其距离,从而提高了修剪操作的安全性和效率。这项研究不仅提高了修剪操作的安全性和效率,还对无人机在农业和林业实践中的自动化发展做出了重要贡献,为环境管理中进一步的创新奠定了基础。
URL
https://arxiv.org/abs/2409.17526