Automatic Sorting of Dwarf Minke Whale Underwater Images

Information

Matthew I. Curnock¹  Marcus Sheaves²  Dmitry A. Konovalov²  R. Alastair Birtles²  Suzanne Hillcoat²  Natalie Swinhoe²  Martha Kusetic²  Genevieve Williams²  Dina B. Efremova³ 

[1] CSIRO Land and Water, James Cook University, Townsville, QLD 4811, Australia;College of Science and Engineering, James Cook University, Townsville, QLD 4181, Australia;Funbox Inc., 119017 Moscow, Russia;

关键词: computer vision;    dwarf minke whales;    convolutional neural networks;    underwater object classification;    image classification;    deep learning;   

DOI  :  10.3390/info11040200

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown