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Sensors 2017, 17(11), 2579;

FieldSAFE: Dataset for Obstacle Detection in Agriculture

Department of Engineering, Aarhus University, Aarhus N 8200, Denmark
Conpleks Innovation ApS, Struer 7600, Denmark
AgroIntelli, Aarhus N 8200, Denmark
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Received: 28 September 2017 / Revised: 6 November 2017 / Accepted: 7 November 2017 / Published: 9 November 2017
(This article belongs to the Special Issue Sensors in Agriculture)
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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 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. View Full-Text
Keywords: dataset; agriculture; obstacle detection; computer vision; cameras; stereo imaging; thermal imaging; LiDAR; radar; object tracking dataset; agriculture; obstacle detection; computer vision; cameras; stereo imaging; thermal imaging; LiDAR; radar; object tracking

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material

  • Externally hosted supplementary file 1
    Description: Website for the FieldSAFE dataset including downloads and descriptions of usage.

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Kragh, M.F.; Christiansen, P.; Laursen, M.S.; Larsen, M.; Steen, K.A.; Green, O.; Karstoft, H.; Jørgensen, R.N. FieldSAFE: Dataset for Obstacle Detection in Agriculture. Sensors 2017, 17, 2579.

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