FieldSAFE: Dataset for Obstacle Detection in Agriculture
This dataset addresses the need for obstacle detection in agricultural automation, but it is incremental as it primarily provides a new resource rather than a methodological breakthrough.
The paper tackles the problem of obstacle detection in agriculture by introducing FieldSAFE, a novel multi-modal dataset with approximately 2 hours of raw sensor data from a tractor-mounted system, including ground truth labels for various obstacles.
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, rocks, barrels, buildings, vehicles, and vegetation. All obstacles have ground truth object labels and geographic coordinates.