AI Fish
Counting
We develop computer vision models to automatically detect, segment and count fish. Trained on regional data, they aim to build autonomous ichthyological monitoring systems for freshwater environments.
Can AI count fish reliably?
Fish counting is a central problem in ichthyology, fisheries management and freshwater conservation. Today it is usually done manually: a person in front of a screen, counting frame by frame. It is slow, expensive and difficult to scale.
We want to know if modern computer vision models can do it autonomously and with sufficient accuracy for real conservation and fisheries applications. This project exists to answer that question with data, not intuition.
Harder than it looks
Counting fish in video remains an open problem. Each environment introduces conditions that generic models handle poorly.
Occlusion
Fish constantly overlap. A fish partially hidden by another generates false negatives or duplicate counts. Instance segmentation must resolve which pixels belong to which individual even when they share visual space.
Camera angles
In confined spaces — channels, indoor pools, inspection chambers — the camera rarely has a clean overhead view. Oblique angles distort fish shape and hinder both detection and species classification.
Confined indoor spaces
Indoor pools and passage ponds create high-density situations: many fish in little space, rapid movement, surface reflections, and variable lighting conditions. None of this appears in standard datasets.
Visual variability
The same fish looks different depending on water turbidity, lighting, angle and stage of its migratory cycle. A model trained under laboratory conditions fails in the field. We are building datasets from real-world conditions.
Detection, segmentation and classification
Detection
Identify the presence and position of each fish in the frame, including partially occluded individuals.
Segmentation
Delineate the exact contour of each individual to separate overlapping fish and obtain accurate counts.
Classification
Identify the species of each detected individual, building models trained on regional freshwater species.
Why counting fish matters
Automatic fish counting has direct applications in fisheries management, biodiversity monitoring and river conservation. Knowing how many individuals of which species pass through a point in a given period remains scarce information, and is rarely obtained continuously and reliably.
Fish lifts, ladders, passage channels and monitoring stations are environments where an autonomous and accurate system could transform the quality of data available for conservation. We are building that technical capability.
We are working on this
We already have video, annotation data in progress and models in training. Current models perform well in low-density scenes with moderate occlusion. Complex cases — high density, difficult angles and severe occlusion — remain the primary focus of the work.
We do not have a finished product. We have a capability under construction, real data and clarity about the problems that remain to be solved.
Do you work in fish monitoring or fisheries?
If you are facing a counting, identification or ichthyological monitoring problem, let's talk.
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