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Volume 11
Issue 4
10.3390/info11040200
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Article

Automatic Sorting of Dwarf Minke Whale Underwater Images †

by
Dmitry A. Konovalov
1,2,*,‡,
Natalie Swinhoe
1,‡,
Dina B. Efremova
3,‡,
R. Alastair Birtles
1,
Martha Kusetic
1,
Suzanne Hillcoat
1,
Matthew I. Curnock
4,
Genevieve Williams
1 and
Marcus Sheaves
1,2
1
College of Science and Engineering, James Cook University, Townsville, QLD 4181, Australia
2
Marine Data Technology Hub, James Cook University, Townsville, QLD 4811, Australia
3
Funbox Inc., 119017 Moscow, Russia
4
CSIRO Land and Water, James Cook University, Townsville, QLD 4811, Australia
*
Author to whom correspondence should be addressed.
†
This paper is an extended version of our paper published in 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, 14–19 July 2019.
‡
These authors contributed equally to this work.
Information 2020, 11(4), 200; https://doi.org/10.3390/info11040200
Received: 25 February 2020 / Revised: 4 April 2020 / Accepted: 5 April 2020 / Published: 9 April 2020
(This article belongs to the Special Issue Computational Sport Science and Sport Analytics)
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Abstract

A predictable aggregation of dwarf minke whales (Balaenoptera acutorostrata subspecies) occurs annually in the Australian waters of the northern Great Barrier Reef in June–July, which has been the subject of a long-term photo-identification study. Researchers from the Minke Whale Project (MWP) at James Cook University collect large volumes of underwater digital imagery each season (e.g., 1.8TB in 2018), much of which is contributed by citizen scientists. Manual processing and analysis of this quantity of data had become infeasible, and Convolutional Neural Networks (CNNs) offered a potential solution. Our study sought to design and train a CNN that could detect whales from video footage in complex near-surface underwater surroundings and differentiate the whales from people, boats and recreational gear. We modified known classification CNNs to localise whales in video frames and digital still images. The required high classification accuracy was achieved by discovering an effective negative-labelling training technique. This resulted in a less than 1% false-positive classification rate and below 0.1% false-negative rate. The final operation-version CNN-pipeline processed all videos (with the interval of 10 frames) in approximately four days (running on two GPUs) delivering 1.95 million sorted images. View Full-Text
Keywords: computer vision; dwarf minke whales; convolutional neural networks; underwater object classification; image classification; deep learning computer vision; dwarf minke whales; convolutional neural networks; underwater object classification; image classification; deep learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
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MDPI and ACS Style

Konovalov, D.A.; Swinhoe, N.; Efremova, D.B.; Birtles, R.A.; Kusetic, M.; Hillcoat, S.; Curnock, M.I.; Williams, G.; Sheaves, M. Automatic Sorting of Dwarf Minke Whale Underwater Images . Information 2020, 11, 200. https://doi.org/10.3390/info11040200

AMA Style

Konovalov DA, Swinhoe N, Efremova DB, Birtles RA, Kusetic M, Hillcoat S, Curnock MI, Williams G, Sheaves M. Automatic Sorting of Dwarf Minke Whale Underwater Images . Information. 2020; 11(4):200. https://doi.org/10.3390/info11040200

Chicago/Turabian Style

Konovalov, Dmitry A.; Swinhoe, Natalie; Efremova, Dina B.; Birtles, R. A.; Kusetic, Martha; Hillcoat, Suzanne; Curnock, Matthew I.; Williams, Genevieve; Sheaves, Marcus. 2020. "Automatic Sorting of Dwarf Minke Whale Underwater Images " Information 11, no. 4: 200. https://doi.org/10.3390/info11040200

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