Video Diver Dataset (VDD-C) 100,000 annotated images of divers underwater

de Langis, Karin
Fulton, Michael
Sattar, Junaed
2021-04-19
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Persistent link to this item

https://doi.org/10.13020/6qrp-wy09
 https://hdl.handle.net/11299/219383
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Collection period

2016-01-10
2020-01-20

Date completed

2020-11-1

Date updated

Time period coverage

Geographic coverage

Source information

Journal Title

Journal ISSN

Volume Title

Title

Video Diver Dataset (VDD-C) 100,000 annotated images of divers underwater

Published Date

2021-04-19

Authors

Group

Interactive Robotics and Vision Lab

Author Contact

Fulton, Michael
fulto081@umn.edu

Type

Dataset
Other Dataset

Abstract

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.

Description

The data of VDDC comes in four zip files: - original_data.zip: Contains the original images and .xml label files, along with some information required to process the data into the proper formats. - script.zip: Contains the script used to generate the labels and images folders from the original_data. - labels.zip: 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. - images.zip: Contains the images of the dataset, filtered to remove poor quality images.

Referenced by

https://arxiv.org/abs/2012.05701

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License

Attribution-ShareAlike 3.0 United States
 http://creativecommons.org/licenses/by-sa/3.0/us/

Publisher

Collections

Data Repository for U of M (DRUM)

Funding information

National Science Foundation #1845364 & #00074041
MNRI Seed Grant

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Previously Published Citation

Other identifiers

Suggested citation

de Langis, Karin; Fulton, Michael; Sattar, Junaed. (2021). Video Diver Dataset (VDD-C) 100,000 annotated images of divers underwater. Retrieved from the Data Repository for the University of Minnesota (DRUM), https://doi.org/10.13020/6qrp-wy09.

View/Download File

File View/Open
Description
Size
yolo_labels.zip
YOLO style labels for VDD-C
(27.82 MB)

voc_labels.zip
VOC style labels for VDD-C
(42.05 MB)

tfrecord_labels.zip
tfrecord style labels for VDD-C
(7.58 GB)

tfsequence_labels_test-set.zip
tfsequence style labels for VDD-C, specifically the test set
(11.79 GB)

tfsequence_labels_val-set.zip
tfsequence style labels for VDD-C, specifically the validation set
(10.24 GB)

tfsequence_labels_train-set-1.zip.part
tfsequence style labels for VDD-C, specifically the training set, part 1/7
(8 GB)

tfsequence_labels_train-set-2.zip.part
tfsequence style labels for VDD-C, specifically the training set, part 2/7
(8 GB)

tfsequence_labels_train-set-3.zip.part
tfsequence style labels for VDD-C, specifically the training set, part 3/7
(8 GB)

tfsequence_labels_train-set-4.zip.part
tfsequence style labels for VDD-C, specifically the training set, part 4/7
(8 GB)

tfsequence_labels_train-set-5.zip.part
tfsequence style labels for VDD-C, specifically the training set, part 5/7
(8 GB)

tfsequence_labels_train-set-6.zip.part
tfsequence style labels for VDD-C, specifically the training set, part 6/7
(8 GB)

tfsequence_labels_train-set-7.zip.part
tfsequence style labels for VDD-C, specifically the training set, part 7/7
(5.26 GB)

images.zip
Processed images of VDD-C
(7.63 GB)

README_VDDC.txt
Readme for VDD-C.
(7.16 KB)

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