KEvOr dataset [Research data]
Issue date
2020-11Author
Suggested citation
Gené Mola, Jordi;
Llorens Calveras, Jordi;
Rosell Polo, Joan Ramon;
Gregorio López, Eduard;
Arnó Satorra, Jaume;
Solanelles Batlle, Francesc;
...
Escolà i Agustí, Alexandre.
(2020)
.
KEvOr dataset [Research data].
Universitat de Lleida.
https://doi.org/10.5281/zenodo.4286460.
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Show full item recordAbstract
The Kinect Evaluation in Orchard conditions (KEvOr) dataset is comprised of a set of RGB-D captures carried out with the Microsoft Kinect v2 to evaluate the performance of this sensor at different lighting conditions in agricultural orchards and from different distances to the measured target.
Three Microsoft Kinect v2 sensors (K2S1, K2S2 and K2S3) were used to scan Fuji apple trees along the afternoon and evening, from the higher sun illuminance (55000 lux) until achieving dark conditions (0.1 lux), obtaining a total of 252 captures: 28 lighting conditions * 3 sensors * 3 repetitions. The data provided for each capture is: the acquired point cloud, illuminance level at the center of the measured scene, and wind speed.
The sensors where placed as follows:
K2S1: Oriented to the north, measuring the row of trees side under direct sunlight. This sensor was placed at 2.5 m from the measured target.
K2S2: Oriented to the north, measuring the row of trees side under direct sunlight. This sensor was placed at 1.5 m from the measured target.
K2S3 Oriented to the south, measuring the row of trees side under indirect sunlight. This sensor was placed at 2.5 m from the measured target.
The dataset also includes two additional registered captures measuring the same scene from 1.5 m and from 2.5 m.
Note
Dades primàries associades a un article publicat a la revista Sensors disponible a l'adreça https://doi.org/10.3390/s20247072Related resource
http://hdl.handle.net/10459.1/70097European research projects
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The following license files are associated with this item:
Except where otherwise noted, this item's license is described as cc-by-nc-sa (c) Jordi Gené et al., 2020
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