A robust and intuitive AI system for researchers to annotate imagery and to train and evaluate deep learning models to monitor coastal and marine species of interest
Reef Sharks schooling among colorful coral reef in clear blue oc
Who will benefit
Researchers, federal and state government, farmers, environmental educators and tourism operators

The Challenge

Current methods of monitoring fish populations involve high costs for manually processing and extracting data from underwater cameras. The FishID project aims to transform environmental monitoring of aquatic ecosystems in Australia through automated detection and identification of animals in underwater imagery.

The Response

The FishID platform has overcome the cost associated with manually processing and extracting data from underwater cameras by creating a user-friendly, public-facing end-to-end pipeline for deep learning detection and automated identification of animals.

FishID is a robust and intuitive system for researchers to annotate imagery, train and evaluate deep learning models to accurately detect, identify and count species of interest across coastal and marine ecosystems.

The project involved the following elements:

  • an automated, integrated service for object detection and classification of underwater imagery including an annotation tool, video analysis and output, improved user interface and public API
  • training, including videos, documentation and full training packages to support uptake and use.
  • community, where software testing and workshops was carried out with users.

The Outcomes

Use FishID, which fills a critical infrastructure gap between the collection of underwater imagery and the resulting data made available in national repositories. It will enable a step-change in monitoring efficiency that will improve outcomes across multiple spaces, including:

Marine environmental monitoring

Including environmental assessment and State of Environment reporting

River health monitoring

Including by the Murray-Darling Basin Authority

Fisheries assessments

Including by state fisheries departments

Aquatic habitat restoration

NGOs are investing $130 million in reef restoration over 5 years. Some already have streaming cameras suitable for automated image analysis.


Including through Moreton Bay live streaming cameras by the Queensland Environmental Education Centre


Including through Great Barrier Reef live streaming cameras with Cairns Aquarium

Who Will Benefit

Researchers, federal and state government, farmers, environmental educators and tourism operators will benefit from the project’s core features:

Integrated end-to-end automation service

This includes a pipeline of software packages that assist annotations using machine learning (ML) and support model creation and testing of deep learning models for object detection and classification. It provides species identification and abundance data outputted in common database formats (e.g. for GlobalArchive).

Online training

Comprehensive training package designed as user-friendly tutorials for topics including annotation, model development and model evaluation

Community building

This involves sharing knowledge among the network of Australian experts through regular workshops with research data scientists and aquatic ecosystem scientists. The project will enable users to access and use models and workflows by making available code through easy-to-understand JupyterLab Notebooks. These are likely to be deployed and run in the EcoCommons command-line environment, EcoCloud.

The Partners

  • Griffith University
  • The University of Adelaide
  • University of the Sunshine Coast
  • The University of Western Australia
  • James Cook University
  • Education Queensland (Moreton Bay Environmental Education Centre)
  • WA Department of Primary Industries and Regional Development

Contact the ARDC

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December 2020 to December 2022

Current Phase


ARDC Co-investment


Project lead

Griffith University