Drone Stereo Vision for Radiata Pine Branch Detection and Distance Measurement: Integrating SGBM and Segmentation Models

TL;DR

This paper presents a drone-based system using stereo vision, deep learning, and semi-global matching to detect and measure radiata pine branches for automated pruning, enhancing safety and efficiency in forestry.

Contribution

It introduces an integrated drone system combining YOLO, Mask R-CNN, and SGBM for precise branch detection and distance measurement in forestry applications.

Findings

High accuracy in branch detection and distance estimation

Effective integration of deep learning and stereo vision techniques

Potential for safer, automated forestry pruning operations

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.