• myGriffith
    • Staff portal
    • Contact Us⌄
      • Future student enquiries 1800 677 728
      • Current student enquiries 1800 154 055
      • International enquiries +61 7 3735 6425
      • General enquiries 07 3735 7111
      • Online enquiries
      • Staff phonebook
    View Item 
    •   Home
    • Griffith Research Online
    • Journal articles
    • View Item
    • Home
    • Griffith Research Online
    • Journal articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

  • All of Griffith Research Online
    • Communities & Collections
    • Authors
    • By Issue Date
    • Titles
  • This Collection
    • Authors
    • By Issue Date
    • Titles
  • Statistics

  • Most Popular Items
  • Statistics by Country
  • Most Popular Authors
  • Support

  • Contact us
  • FAQs
  • Admin login

  • Login
  • Deep learning for automated analysis of fish abundance: the benefits of training across multiple habitats

    Thumbnail
    View/Open
    Connolly446691-Accepted.pdf (290.3Kb)
    File version
    Accepted Manuscript (AM)
    Author(s)
    Ditria, Ellen M
    Sievers, Michael
    Lopez-Marcano, Sebastian
    Jinks, Eric L
    Connolly, Rod M
    Griffith University Author(s)
    Connolly, Rod M.
    Lopez Marcano, Sebastian E.
    Sievers, Michael K.
    Year published
    2020
    Metadata
    Show full item record
    Abstract
    Environmental monitoring guides conservation and is particularly important for aquatic habitats which are heavily impacted by human activities. Underwater cameras and uncrewed devices monitor aquatic wildlife, but manual processing of footage is a significant bottleneck to rapid data processing and dissemination of results. Deep learning has emerged as a solution, but its ability to accurately detect animals across habitat types and locations is largely untested for coastal environments. Here, we produce five deep learning models using an object detection framework to detect an ecologically important fish, luderick (Girella ...
    View more >
    Environmental monitoring guides conservation and is particularly important for aquatic habitats which are heavily impacted by human activities. Underwater cameras and uncrewed devices monitor aquatic wildlife, but manual processing of footage is a significant bottleneck to rapid data processing and dissemination of results. Deep learning has emerged as a solution, but its ability to accurately detect animals across habitat types and locations is largely untested for coastal environments. Here, we produce five deep learning models using an object detection framework to detect an ecologically important fish, luderick (Girella tricuspidata). We trained two models on footage from single habitats (seagrass or reef) and three on footage from both habitats. All models were subjected to tests from both habitat types. Models performed well on test data from the same habitat type (object detection measure: mAP50: 91.7 and 86.9% performance for seagrass and reef, respectively) but poorly on test sets from a different habitat type (73.3 and 58.4%, respectively). The model trained on a combination of both habitats produced the highest object detection results for both tests (an average of 92.4 and 87.8%, respectively). The ability of the combination trained models to correctly estimate the ecological abundance metric, MaxN, showed similar patterns. The findings demonstrate that deep learning models extract ecologically useful information from video footage accurately and consistently and can perform across habitat types when trained on footage from the variety of habitat types.
    View less >
    Journal Title
    Environmental Monitoring and Assessment
    Volume
    192
    Issue
    11
    DOI
    https://doi.org/10.1007/s10661-020-08653-z
    Copyright Statement
    © 2020 Springer Netherlands. This is an electronic version of an article published in Environmental Monitoring and Assessment, 2020, 192 (11), pp. 698. Environmental Monitoring and Assessment is available online at: http://link.springer.com/ with the open URL of your article.
    Subject
    Environmental sciences
    Science & Technology
    Life Sciences & Biomedicine
    Computer vision
    Ecology
    Publication URI
    http://hdl.handle.net/10072/400370
    Collection
    • Journal articles

    Footer

    Disclaimer

    • Privacy policy
    • Copyright matters
    • CRICOS Provider - 00233E
    • TEQSA: PRV12076

    Tagline

    • Gold Coast
    • Logan
    • Brisbane - Queensland, Australia
    First Peoples of Australia
    • Aboriginal
    • Torres Strait Islander