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Fruit detection and 3D location using instance segmentation neural networks and structure-from-motion photogrammetry

Author
Gené, J.; Sanz, R.; Rosell, J.R.; Morros, J.R.; Ruiz-Hidalgo, J.; Vilaplana, V.; Gregorio, E.
Type of activity
Journal article
Journal
Computers and electronics in agriculture
Date of publication
2020-01-13
Volume
169
Number
105165
First page
105165:1
Last page
105165:21
DOI
10.1016/j.compag.2019.105165
Project funding
Multimodal Signal Processing and Machine Learning on Graphs
Repository
http://hdl.handle.net/2117/175526 Open in new window
URL
https://www.sciencedirect.com/science/article/pii/S0168169919321507 Open in new window
Abstract
The development of remote fruit detection systems able to identify and 3D locate fruits provides opportunities to improve the efficiency of agriculture management. Most of the current fruit detection systems are based on 2D image analysis. Although the use of 3D sensors is emerging, precise 3D fruit location is still a pending issue. This work presents a new methodology for fruit detection and 3D location consisting of: (1) 2D fruit detection and segmentation using Mask R-CNN instance segmentati...
Citation
Gené, J. [et al.]. Fruit detection and 3D location using instance segmentation neural networks and structure-from-motion photogrammetry. "Computers and electronics in agriculture", 13 Gener 2020, vol. 169, núm. 105165, p. 105165:1-105165:21.
Keywords
Fruit detection, Fruit location, Mask R-CNN, Structure-from-motion, Terrestrial remote sensing
Group of research
GPI - Image and Video Processing Group
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center

Participants