New interactive machine learning tool for marine image analysis

Author:

Clark H. Poppy1ORCID,Smith Abraham George2,McKay Fletcher Daniel3,Larsson Ann I.4,Jaspars Marcel1ORCID,De Clippele Laurence H.5

Affiliation:

1. Marine Biodiscovery Centre, Department of Chemistry, University of Aberdeen , Aberdeen AB24 3UE, UK

2. Department of Computer Science, University of Copenhagen , Copenhagen 2100, Denmark

3. Rural Economy, Environment and Society, Scotland’s Rural College , Edinburgh EH9 3JG, UK

4. Tjärnö Marine Laboratory, Department of Marine Sciences, University of Gothenburg , Sweden

5. School of Biodiversity, One Health & Veterinary Medicine, University of Glasgow , Glasgow G61 1QH, UK

Abstract

Advancing imaging technologies are drastically increasing the rate of marine video and image data collection. Often these datasets are not analysed to their full potential as extracting information for multiple species is incredibly time-consuming. This study demonstrates the capability of the open-source interactive machine learning tool, RootPainter , to analyse large marine image datasets quickly and accurately. The ability of RootPainter to extract the presence and surface area of the cold-water coral reef associate sponge species, Mycale lingua , was tested in two datasets: 18 346 time-lapse images and 1420 remotely operated vehicle video frames. New corrective annotation metrics integrated with RootPainter allow objective assessment of when to stop model training and reduce the need for manual model validation. Three highly accurate M. lingua models were created using RootPainter , with an average dice score of 0.94 ± 0.06. Transfer learning aided the production of two of the models, increasing analysis efficiency from 6 to 16 times faster than manual annotation for time-lapse images. Surface area measurements were extracted from both datasets allowing future investigation of sponge behaviours and distributions. Moving forward, interactive machine learning tools and model sharing could dramatically increase image analysis speeds, collaborative research and our understanding of spatiotemporal patterns in biodiversity.

Funder

Rural Environment Science and Analytical Services Division

European Union Horizon 2020 iAtlantic project

Novo Nordisk

Biotechnology and Biological Sciences Research Council EASTBIO Doctoral Training Programme

Publisher

The Royal Society

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