Why are BRUVS important?
Monitoring fish biodiversity and biomass are the best indicator of ecological health
Why does BRUVS need to be automated?
What is AFID?
A new Automated Fish Identification (AFID) platform is being developed for active machine learning and integrated into EventMeasure: AFID will be an active learning feedback system by taking expertly curated data from EventMeasure to train the latest machine learning algorithms. When a fish ecologist annotates BRUVS, EventMeasure will automatically query AFID for identified species, potential Max-N frames as well as a running MaxN count. The user will then have the ability to accept AFID classification and length measurements or correct any errors. When the user has completed their analysis, EventMeasure will report the results to AFID which will automatically re-train and relearn from the corrected annotations. This will result in AFID becoming more accurate over time as more analysis is completed and more data generated for machine learning algorithms.
Why do we need AFID?
The key challenges of AFID will be addressed and solved through the combination of machine learning algorithms with novel sensor technology in order to harness the expertise of trained ecologists. Our solution will automate fish length measurements, pre-screen videos, find potential MaxN frames, total fish count estimates and group similarly looking fish for fast bulk labelling. The outputs from machine learning and sensor technology will be incorporated into EventMeasure, ensuring continuity with current BRUVS workflows and future proofing through adaptation to emerging machine learning technologies with minimal disruption to current BRUVS workflows
How is AFID different?
AFID is an opensource Django based, Autonomous Fish Identification AFID platform. AFID can be run as a stand alone desktop application or hosted on a cloud platform such as Amazon Web Services (AWS). AFID will handle all of the autonomous machine learning analysis. AFID will include a plug-in system that will allow different machine learning algorithms to be used and customised as required by the customer, who may wish to design their own. AFID will be designed to work closely with EventMeasure through an Application Programming Interface (API). EventMeasure will continue to be adapted to incorporate AFID generated classifications and length measurements as well as export human based classifications back to AFID in order for AFID to retrain, relearn and improve - achieving active machine learning.
Why is opensource important?
As the members of our consortium have a background in research and see the value of making BRUVS research accessible to all scientific researchers. AFID and machine learning code will remain open source for research and non-profit use. This will prevent ‘vendor lock in’ and allow both ecologists and data scientists to use, contribute and improve the ML algorithms. Our open source approach is the major reason we have gained the support and commitment of time from the Australian BRUVS working group.