AFID

Automated Fish Identification

The Digital Assistant for EventMeasure

Project Mission

Reduce the cost and manual labour required to monitor our sensitive maritime ecosystem through data science

Machine Learning Based Digital Assistant

The AFID Digital assistant is being developed for EventMeasure

AFID uses Machine Learning to suggest species right inside EventMeasure

AFID autonomously detects the head and tail of fish and uses EventMeasure's calibration methods to accurately measure the length of the fish.

Automated Fish Length Measurements

Automated Species Classification

Relative Species Abundance

Finding MaxN frames, running MaxN and average MaxN


Open Source

Open access to researchers

Github: Coming soon


Questions?

Find out more about AFID

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AFID is currently in the proof of concept stage of the BRII - AIMS challenge. If you have data to contribute to public datasets, time to contribute to machine learning algorithms, or would like to be part of our stakeholder group, please contact Dan Marrable to get more information about the project

Project Partners :

With Support From: