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PointNet and geometric reasoning for detection of grape vines from single frame RGB-D data in outdoor conditions
Centre for Applied Autonomous Sensor Systems, Örebro University, Örebro, Sweden.ORCID iD: 0000-0003-4685-379X
Umeå University, Faculty of Science and Technology, Department of Computing Science.ORCID iD: 0000-0002-4600-8652
Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva, Israel.ORCID iD: 0000-0001-6265-4497
Centre for Applied Autonomous Sensor Systems, Örebro University, Örebro, Sweden.ORCID iD: 0000-0002-2953-1564
2020 (English)In: Proceedings of the Northern Lights Deep Learning Workshop, Septentrio Academic Publishing , 2020, Vol. 1, p. 1-6Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we present the usage of PointNet, a deep neural network that consumes raw un-ordered point clouds, for detection of grape vine clusters in outdoor conditions. We investigate the added value of feeding the detection network with both RGB and depth, contradictory to common practice in agricultural robotics of relying on RGB only. A total of 5057 pointclouds (1033 manually annotated and 4024 annotated using geometric reasoning) were collected in a field experiment conducted in outdoor conditions on 9 grape vines and 5 plants. The detection results show overall accuracy of 91% (average class accuracy of 74%, precision 53% recall 48%) for RGBXYZ data and a significant drop in recall for RGB or XYZ data only. These results suggest the usage of depth cameras for vision in agricultural robotics is crucial for crops where the color contrast between the crop and the background is complex. The results also suggest geometric reasoning can be used for increased training set size, a major bottleneck in the development of agricultural vision systems.

Place, publisher, year, edition, pages
Septentrio Academic Publishing , 2020. Vol. 1, p. 1-6
Keywords [en]
RGBD, Deep-learning, Agricultural robotics, outdoor vision, grape
National Category
Computer Vision and Robotics (Autonomous Systems) Other Agricultural Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:umu:diva-177113DOI: 10.7557/18.5155OAI: oai:DiVA.org:umu-177113DiVA, id: diva2:1504352
Conference
3rd Northern Lights Deep Learning Workshop, Tromsö, Norway, January 19-21, 2020.
Available from: 2020-11-27 Created: 2020-11-27 Last updated: 2022-08-24Bibliographically approved

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Kurtser, PolinaRingdahl, OlaAndreasson, Henrik

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