Optical flow-based branch segmentation for complex orchard environments

  title={Optical flow-based branch segmentation for complex orchard environments},
  author={Alexander You and Cindy Grimm and Joseph R. Davidson},
  journal={2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
Machine vision is a critical subsystem for enabling robots to be able to perform a variety of tasks in orchard environments. However, orchards are highly visually complex environments, and computer vision algorithms operating in them must be able to contend with variable lighting conditions and background noise. Past work on enabling deep learning algorithms to operate in these environments has typically required large amounts of hand-labeled data to train a deep neural network or physically… 

Figures and Tables from this paper

An autonomous robot for pruning modern, planar fruit trees

This paper introduces a system for pruning sweet cherry trees (in a planar tree architecture called an upright fruiting offshoot offshoot configuration) that integrates various subsystems from previous work on perception and manipulation and is capable of operating completely autonomously and requires minimal control of the environment.



Deep learning based segmentation for automated training of apple trees on trellis wires

SegFlow: Joint Learning for Video Object Segmentation and Optical Flow

This paper proposes an end-to-end trainable network, SegFlow, for simultaneously predicting pixel-wise object segmentation and optical flow in videos, and demonstrates that introducing optical flow improves the performance of segmentation, against the state-of-the-art algorithms.

Semantic Segmentation for Partially Occluded Apple Trees Based on Deep Learning

FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

The concept of end-to-end learning of optical flow is advanced and it work really well, and faster variants that allow optical flow computation at up to 140fps with accuracy matching the original FlowNet are presented.

Computer vision‐based tree trunk and branch identification and shaking points detection in Dense‐Foliage canopy for automated harvesting of apples

Fresh market apples are one of the high‐value crops in the United States. Washington alone has produced two‐thirds of the annual national production in the past 10 years. However, the availability of

Background Subtraction in Real Applications: Challenges, Current Models and Future Directions

Integrated detection of citrus fruits and branches using a convolutional neural network

A robotic vision system to measure tree traits

  • A. TabbHenry Medeiros
  • Computer Science
    2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • 2017
A robotic vision system called the Robotic System for Tree Shape Estimation (RoTSE) to determine tree traits in field settings is proposed and results on apple trees are shown in terms of accuracy, computation time, and robustness.

Video Object Segmentation and Tracking: A Survey

A comprehensive review of the state-of-the-art tracking methods, and classify these methods into different categories, and identify new trends is provided.