Video Diver Dataset (VDD-C̅)

  • Karin J De Langis (Creator)
  • Michael S Fulton (Creator)
  • Junaed Sattar (Creator)



This dataset contains over 100,000 annotated images of divers underwater, gathered from videos of divers in pools and the Caribbean off the coast of Barbados. It is intended for the development and testing of diver detection algorithms for use in autonomous underwater vehicles (AUVs). Because the images are sourced from videos, they are largely sequential, meaning that temporally aware algorithms (video object detectors) can be trained and tested on this data. Training on this data improved our current diver detection algorithms significantly because we increased our training set size by 17 times compared to our previous best dataset. It is released for free for anyone who wants to use it.

The data of VDDC comes in four zip files: - Contains the original images and .xml label files, along with some information required to process the data into the proper formats. - Contains the script used to generate the labels and images folders from the original_data. - Contains a variety of label types, in voc, yolo, tfrecord, and tfsequence formats. These labels are also properly filtered to correct inaccurate coordinates for annotations and remove unwanted annotations. - Contains the images of the dataset, filtered to remove poor quality images.

Funding information
Sponsorship: National Science Foundation #1845364 & #00074041; MNRI Seed Grant

Referenced by
Date made availableApr 19 2021
PublisherData Repository for the University of Minnesota
Date of data productionJan 10 2016 - Jan 20 2020

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