YOLO and SGBM Integration for Autonomous Tree Branch Detection and Depth Estimation in Radiata Pine Pruning Applications

TL;DR

This paper introduces a computer vision system combining YOLO and SGBM for autonomous drone-based pruning of radiata pine, enabling accurate branch detection and depth estimation without expensive sensors, thus improving safety and efficiency.

Contribution

The novel integration of YOLO with SGBM for real-time branch detection and depth estimation in forestry applications using stereo vision.

Findings

YOLO outperforms Mask R-CNN with 82.0% mAP.

System localizes branches within 2 meters.

Processing time is under one second per frame.

Abstract

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.