FieldSAFE: Dataset for Obstacle Detection in Agriculture

Mikkel Kragh, Peter Christiansen, Morten Laursen, Morten Larsen, Kim Steen, Ole Green, Henrik Karstoft, Rasmus Jørgensen
2017 Sensors  
In this paper, we present a novel multi-modal dataset for obstacle detection in agriculture. The dataset comprises approximately 2 hours 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-degree 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,
more » ... cks, barrels, buildings, vehicles, and vegetation. All obstacles have ground truth object labels and geographic coordinates.
doi:10.3390/s17112579 pmid:29120383 pmcid:PMC5713196 fatcat:fnjzk6f23rdi7lgzxgsspfpodq