Drone Stereo Vision for Radiata Pine Branch Detection and Distance Measurement: Utilizing Deep Learning and YOLO Integration

TL;DR

This paper presents a drone-based system using deep learning and stereo vision to detect and measure tree branches accurately, improving automation in pruning with enhanced depth estimation techniques.

Contribution

It introduces a novel integration of YOLO and deep learning for precise branch detection and depth measurement without ground-truth data, advancing agricultural automation.

Findings

Deep learning yields more accurate depth maps than traditional methods.

Fine-tuning neural networks improves depth estimation in absence of ground-truth.

The system enhances pruning automation with higher accuracy and efficiency.

Abstract

This research focuses on the development of a drone equipped with pruning tools and a stereo vision camera to accurately detect and measure the spatial positions of tree branches. YOLO is employed for branch segmentation, while two depth estimation approaches, monocular and stereo, are investigated. In comparison to SGBM, deep learning techniques produce more refined and accurate depth maps. In the absence of ground-truth data, a fine-tuning process using deep neural networks is applied to approximate optimal depth values. This methodology facilitates precise branch detection and distance measurement, addressing critical challenges in the automation of pruning operations. The results demonstrate notable advancements in both accuracy and efficiency, underscoring the potential of deep learning to drive innovation and enhance automation in the agricultural sector.