FieldSAFE: Dataset for Obstacle Detection in Agriculture

In this paper, we present a multi-modal dataset for obstacle detection in agriculture. The dataset comprises approximately 2 h of raw sensor data from a tractor-mounted sensor system in a grass mowing scenario in Denmark, October 2016. Sensing modalities include stereo camera, thermal camera, web ca...

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Detalles Bibliográficos
Autores principales: Kragh, Mikkel Fly, Christiansen, Peter, Laursen, Morten Stigaard, Larsen, Morten, Steen, Kim Arild, Green, Ole, Karstoft, Henrik, Jørgensen, Rasmus Nyholm
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5713196/
https://www.ncbi.nlm.nih.gov/pubmed/29120383
http://dx.doi.org/10.3390/s17112579
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author Kragh, Mikkel Fly
Christiansen, Peter
Laursen, Morten Stigaard
Larsen, Morten
Steen, Kim Arild
Green, Ole
Karstoft, Henrik
Jørgensen, Rasmus Nyholm
author_facet Kragh, Mikkel Fly
Christiansen, Peter
Laursen, Morten Stigaard
Larsen, Morten
Steen, Kim Arild
Green, Ole
Karstoft, Henrik
Jørgensen, Rasmus Nyholm
author_sort Kragh, Mikkel Fly
collection PubMed
description In this paper, we present a multi-modal dataset for obstacle detection in agriculture. The dataset comprises approximately 2 h of raw sensor data from a tractor-mounted sensor system in a grass mowing scenario in Denmark, October 2016. Sensing modalities include stereo camera, thermal camera, web camera, 360 [Formula: see text] camera, LiDAR and radar, while precise localization is available from fused IMU and GNSS. Both static and moving obstacles are present, including humans, mannequin dolls, rocks, barrels, buildings, vehicles and vegetation. All obstacles have ground truth object labels and geographic coordinates.
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spelling pubmed-57131962017-12-07 FieldSAFE: Dataset for Obstacle Detection in Agriculture Kragh, Mikkel Fly Christiansen, Peter Laursen, Morten Stigaard Larsen, Morten Steen, Kim Arild Green, Ole Karstoft, Henrik Jørgensen, Rasmus Nyholm Sensors (Basel) Article In this paper, we present a multi-modal dataset for obstacle detection in agriculture. The dataset comprises approximately 2 h of raw sensor data from a tractor-mounted sensor system in a grass mowing scenario in Denmark, October 2016. Sensing modalities include stereo camera, thermal camera, web camera, 360 [Formula: see text] camera, LiDAR and radar, while precise localization is available from fused IMU and GNSS. Both static and moving obstacles are present, including humans, mannequin dolls, rocks, barrels, buildings, vehicles and vegetation. All obstacles have ground truth object labels and geographic coordinates. MDPI 2017-11-09 /pmc/articles/PMC5713196/ /pubmed/29120383 http://dx.doi.org/10.3390/s17112579 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kragh, Mikkel Fly
Christiansen, Peter
Laursen, Morten Stigaard
Larsen, Morten
Steen, Kim Arild
Green, Ole
Karstoft, Henrik
Jørgensen, Rasmus Nyholm
FieldSAFE: Dataset for Obstacle Detection in Agriculture
title FieldSAFE: Dataset for Obstacle Detection in Agriculture
title_full FieldSAFE: Dataset for Obstacle Detection in Agriculture
title_fullStr FieldSAFE: Dataset for Obstacle Detection in Agriculture
title_full_unstemmed FieldSAFE: Dataset for Obstacle Detection in Agriculture
title_short FieldSAFE: Dataset for Obstacle Detection in Agriculture
title_sort fieldsafe: dataset for obstacle detection in agriculture
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5713196/
https://www.ncbi.nlm.nih.gov/pubmed/29120383
http://dx.doi.org/10.3390/s17112579
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