Automatic Detection, Positioning and Counting of Grape Bunches Using Robots

• URL: http://arxiv.org/abs/2412.10464v1
• Date: Thu, 12 Dec 2024 15:52:40 GMT
• Title: Automatic Detection, Positioning and Counting of Grape Bunches Using Robots
• Authors: Xumin Gao,
• Abstract summary: The Yolov3 detection network is used to realize the accurate detection of grape bunches. The local tracking algorithm is added to eliminate relocation. The counting of grape bunches is completed.
• Score: 0.0
• License:
• Abstract: In order to promote agricultural automatic picking and yield estimation technology, this project designs a set of automatic detection, positioning and counting algorithms for grape bunches, and applies it to agricultural robots. The Yolov3 detection network is used to realize the accurate detection of grape bunches, and the local tracking algorithm is added to eliminate relocation. Then it obtains the accurate 3D spatial position of the central points of grape bunches using the depth distance and the spatial restriction method. Finally, the counting of grape bunches is completed. It is verified using the agricultural robot in the simulated vineyard environment. The project code is released at: https://github.com/XuminGaoGithub/Grape_bunches_count_using_robots.

Related papers

• Unsupervised Change Detection for Space Habitats Using 3D Point Clouds [4.642898625014145]
  This work presents an algorithm for scene change detection from point clouds to enable autonomous robotic caretaking in future space habitats. The algorithm is validated quantitatively and qualitatively using a test dataset collected by an Astrobee robot in the NASA Ames Granite Lab.
  arXiv  Detail & Related papers  (2023-12-04T23:26:12Z)
• Care3D: An Active 3D Object Detection Dataset of Real Robotic-Care Environments [52.425280825457385]
  This paper introduces an annotated dataset of real environments. The captured environments represent areas which are already in use in the field of robotic health care research. We also provide ground truth data within one room, for assessing SLAM algorithms running directly on a health care robot.
  arXiv  Detail & Related papers  (2023-10-09T10:35:37Z)
• Neural Implicit Dense Semantic SLAM [83.04331351572277]
  We propose a novel RGBD vSLAM algorithm that learns a memory-efficient, dense 3D geometry, and semantic segmentation of an indoor scene in an online manner. Our pipeline combines classical 3D vision-based tracking and loop closing with neural fields-based mapping. Our proposed algorithm can greatly enhance scene perception and assist with a range of robot control problems.
  arXiv  Detail & Related papers  (2023-04-27T23:03:52Z)
• Vision-based Vineyard Navigation Solution with Automatic Annotation [2.6013566739979463]
  We introduce a vision-based autonomous navigation framework for agriculture robots in trellised cropping systems such as vineyards. We propose a novel learning-based method to estimate the path traversibility heatmap directly from an RGB-D image. A trained path detection model was used to develop a full navigation framework consisting of row tracking and row switching modules.
  arXiv  Detail & Related papers  (2023-03-25T03:37:17Z)
• Construction of Object Boundaries for the Autopilotof a Surface Robot from Satellite Imagesusing Computer Vision Methods [101.18253437732933]
  A method for detecting water objects on satellite maps is proposed. An algorithm for calculating the GPS coordinates of the contours is created. The proposed algorithm allows saving the result in a format suitable for the surface robot autopilot module.
  arXiv  Detail & Related papers  (2022-12-05T12:07:40Z)
• Neural Scene Representation for Locomotion on Structured Terrain [56.48607865960868]
  We propose a learning-based method to reconstruct the local terrain for a mobile robot traversing urban environments. Using a stream of depth measurements from the onboard cameras and the robot's trajectory, the estimates the topography in the robot's vicinity. We propose a 3D reconstruction model that faithfully reconstructs the scene, despite the noisy measurements and large amounts of missing data coming from the blind spots of the camera arrangement.
  arXiv  Detail & Related papers  (2022-06-16T10:45:17Z)
• Geometry-Aware Fruit Grasping Estimation for Robotic Harvesting in Orchards [6.963582954232132]
  geometry-aware network, A3N, is proposed to perform end-to-end instance segmentation and grasping estimation. We implement a global-to-local scanning strategy, which enables robots to accurately recognise and retrieve fruits in field environments. Overall, the robotic system achieves success rate of harvesting ranging from 70% - 85% in field harvesting experiments.
  arXiv  Detail & Related papers  (2021-12-08T16:17:26Z)
• Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems [92.26462290867963]
  Kimera-Multi is the first multi-robot system that is robust and capable of identifying and rejecting incorrect inter and intra-robot loop closures. We demonstrate Kimera-Multi in photo-realistic simulations, SLAM benchmarking datasets, and challenging outdoor datasets collected using ground robots.
  arXiv  Detail & Related papers  (2021-06-28T03:56:40Z)
• FGR: Frustum-Aware Geometric Reasoning for Weakly Supervised 3D Vehicle Detection [81.79171905308827]
  We propose frustum-aware geometric reasoning (FGR) to detect vehicles in point clouds without any 3D annotations. Our method consists of two stages: coarse 3D segmentation and 3D bounding box estimation. It is able to accurately detect objects in 3D space with only 2D bounding boxes and sparse point clouds.
  arXiv  Detail & Related papers  (2021-05-17T07:29:55Z)
• High precision control and deep learning-based corn stand counting algorithms for agricultural robot [8.16286714346538]
  This paper presents high precision control and deep learning-based corn stand counting algorithms for a low-cost, ultra-compact 3D printed and autonomous field robot. The robot, termed TerraSentia, is designed to automate the measurement of plant traits for efficient phenotyping.
  arXiv  Detail & Related papers  (2021-03-21T01:13:38Z)
• Strawberry Detection Using a Heterogeneous Multi-Processor Platform [1.5171938155576565]
  This paper proposes using the You Only Look Once version 3 (YOLOv3) Convolutional Neural Network (CNN) in combination with utilising image processing techniques for the application of precision farming robots. The results show a performance acceleration by five times when implemented on a Field-Programmable Gate Array (FPGA) when compared with the same algorithm running on the processor side.
  arXiv  Detail & Related papers  (2020-11-07T01:08:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.