Automatic sorting of Dwarf Minke Whale underwater images

Konovalov, Dmitry A., Swinhoe, Natalie, Efremova, Dina B., Birtles, R. Alastair, Kusetic, Martha, Hillcoat, Suzanne, Curnock, Matthew I., Williams, Genevieve, and Sheaves, Marcus (2020) Automatic sorting of Dwarf Minke Whale underwater images. Information, 11 (4). 200.

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Abstract: Apredictableaggregationofdwarfminkewhales(Balaenopteraacutorostratasubspecies) 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.

Item ID: 62875
Item Type: Article (Research - C1)
ISSN: 2078-2489
Keywords: computer vision; dwarf minke whales; convolutional neural networks; underwater object classification; image classification; deep learning
Copyright Information: © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an Open Access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
Date Deposited: 06 Aug 2020 05:09
FoR Codes: 46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460306 Image processing @ 100%
SEO Codes: 90 COMMERCIAL SERVICES AND TOURISM > 9003 Tourism > 900399 Tourism not elsewhere classified @ 100%
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