mlsanityData to 14 Oct 2026

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

arXiv 2410.00503 ↗First posted 1 Oct 2024cs.CV cs.AIcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 26 of its 56 references that are in the map. First posted on arXiv 1 Oct 2024.

Builds on

26 of 56 references.

References · 26

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  2. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  3. 2017Mask R-CNNcited by 7,277
  4. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  5. 2015Fast R-CNNcited by 2,933
  6. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,162
  7. 2019Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfercited by 1,495
  8. 2024Depth Anything: Unleashing the Power of Large-Scale Unlabeled Datacited by 1,360
  9. 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,274
  10. 2015A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimationcited by 881
  11. 2014Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognitioncited by 720
  12. 2020Image Segmentation Using Deep Learning: A Surveycited by 660
14 more
  1. 2019Object Detection in 20 Years: A Surveycited by 531
  2. 2018Pyramid Stereo Matching Networkcited by 489
  3. 2024YOLOv9: Learning What You Want to Learn Using Programmable Gradient Informationcited by 425
  4. 2021RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matchingcited by 353
  5. 2016Understanding Deep Neural Networks with Rectified Linear Unitscited by 257
  6. 2019Group-wise Correlation Stereo Networkcited by 235
  7. 2017SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloudcited by 232
  8. 2023MiDaS v3.1 -- A Model Zoo for Robust Monocular Relative Depth Estimationcited by 174
  9. 2022Attention Concatenation Volume for Accurate and Efficient Stereo Matchingcited by 130
  10. 2023Real-Time Flying Object Detection with YOLOv8cited by 94
  11. 2020A Survey on Deep Learning Techniques for Stereo-based Depth Estimationcited by 90
  12. 2021MobileStereoNet: Towards Lightweight Deep Networks for Stereo Matchingcited by 82
  13. 2023NeRF-Supervised Deep Stereocited by 46
  14. 2024Drone Stereo Vision for Radiata Pine Branch Detection and Distance Measurement: Integrating SGBM and Segmentation Modelscited by 3

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Citing papers per year: 2025: 1.

1 paper.

  1. 2025Genetic Algorithms For Parameter Optimization for Disparity Map Generation of Radiata Pine Branch Imagescited by 4

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