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/a6d5a40e-0358-47cf-9ec1-335df0e4a3c3

Sokolova, Maria; Cordova, Manuel; Nap, Henk; van Helmond, Edwin; Mans, Michiel et. al. (2024): Data underlying the publication: An integrated end-to-end deep neural network for automated detection of discarded fish species and their weight estimation. Version 2. 4TU.ResearchData. dataset. https://doi.org/10.4121/a6d5a40e-0358-47cf-9ec1-335df0e4a3c3.v2

Dataset

x choose version: version 1 - 2023-08-03

• Artificial Intelligence and Image Processing
• Fisheries - Wild Caught
• Agricultural and Veterinary Sciences
• Animal Production and Animal Primary Products
• Information and Computing Sciences
• Biological Sciences

computer vision, fisheries, occlusion, weight estimation, YOLOv5

2021

CC BY 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Maria Sokolova , Manuel Cordova , Henk Nap , Edwin van Helmond , Michiel Mans , Arjan Vroegop , Angelo Mencarelli , Gert Kootstra

The dataset contains images of the discarded fish on the conveyor belt and annotations. Annotations are prepared in YOLO format, i.e. separate text files, containing fish species label, object bounding box annotation, weight and occlusion level. Annotation per individual fish is written in a separate row of the file.

Additionally, we provide weight file (.pt) for the best performing Detection-Weight2 model.

• 2023-08-03 first online

• 2024-06-11 published, posted

4TU.ResearchData

image/.png; annotation files/.txt; model weights file/.pt

An integrated end-to-end deep neural network for automated detection of discarded fish species and their weight estimation.

• Fully Documented Fisheries (grant code 16302) European Maritime and Fisheries Fund

Wageningen University and Research, Department of Plant Sciences