Data underlying the publication: Automatic discard registration in cluttered environments using deep learning and object tracking: class imbalance, occlusion, and a comparison to human review

doi: 10.4121/16622566.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/16622566
Datacite citation style:
Rick Essen, van; Arjan Vroegop; A. (Angelo) Mencarelli; Aloysius van Helmond; Linh Nyugen et. al. (2021): Data underlying the publication: Automatic discard registration in cluttered environments using deep learning and object tracking: class imbalance, occlusion, and a comparison to human review. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/16622566.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset
Wageningen University and Research logo
usage stats
926
views
400
downloads
geolocation
North Sea
time coverage
October 2019
licence
cc-by.png logo CC BY 4.0
Data for training and evaluation of a method for detection and counting demersal fish species in complex, cluttered and occluded environments that can be installed on the
conveyor belts of fishing vessels. The data mainly exists of images of fish on a conveyer belt with the corresponding annotations. This was used to train a neural network (YOLOv3) to detect and classify fish species. Because each fish is visible in multiple images, the fishes were tracked over consecutive images and the total number of fish per specie was counted. These counts were compared to human review.
history
  • 2021-10-26 first online, published, posted
publisher
4TU.ResearchData
format
json pt
organizations
Farm Technology Group, Wageningen University and Research;
Greenhouse Horticulture Unit, Wageningen University and Research;
Wageningen Marine Research, Wageningen University and Research;
Aquaculture and Fisheries, Wageningen University and Research

DATA

files (5)