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Government of Canada – DTPR AI Register

AI-Assisted Salmon Counting from Underwater Fence Camera Footage

Ecology

What it collects

About a measurement
Anonymized data
  • Underwater video footage collected at fish fence monitoring sites across rivers and streams, provided by the Science Branch — Stock Assessment Lab. The footage captures fish passing through fence structures and contains no personal information.
Run by
Fisheries and Oceans Canada (DFO)
Where
No fixed location
Kept
Not stated by the Helpful Places.
Shared with
Not stated by the Helpful Places.

What it is for

Ecology

This system uses computer vision AI to automatically detect and count salmon by species from underwater video footage collected at fish fence monitoring sites. It is being developed by Fisheries and Oceans Canada to reduce the manual effort required by scientists who currently review hours of footage by hand. No personal information is involved, and the AI use is disclosed to users. The system is currently under development and not yet in production.

What it collects and what happens to it

Data taken in

About a measurement
Anonymized data
  • Underwater video footage collected at fish fence monitoring sites across rivers and streams, provided by the Science Branch — Stock Assessment Lab. The footage captures fish passing through fence structures and contains no personal information.

Processing

Computer Vision
  • The system uses advanced computer vision techniques to detect and count salmon species from underwater video footage captured at fish fence monitoring structures installed across rivers and streams.

What it does

Sensing (Perceptive AI)
Human decides
  • The system processes underwater video footage to detect and count salmon by species — converting raw video frames into structured species counts. Government of Canada employees (GC employees) review the resulting data, placing human oversight on downstream stock assessment decisions.
Deciding (Analytical AI)
Human decides
  • Beyond raw detection, the system classifies detected fish by species and produces counts — a classification and scoring function over structured detections. Results are reviewed by GC scientists who make final stock assessment determinations.

Outputs

About a measurement
Anonymized data
  • Species-level salmon counts derived from automated analysis of fence camera footage — structured count data indicating the number of salmon detected per species at each monitoring site. This output replaces manual tally records previously produced by human reviewers.

Run by

Fisheries and Oceans Canada (DFO)
  • Fisheries and Oceans Canada is the federal department responsible for safeguarding Canadian waters and aquatic resources. DFO deploys and operates PSSIFence to support its fish stock assessment obligations.

Government of Canada AI Register — PSSIFence (2526-DFO-MPO-007)

Built by

Government of Canada
  • The system was developed internally by the Government of Canada, as indicated in the register entry under 'Developed by'.

Government of Canada AI Register — PSSIFence (2526-DFO-MPO-007)

Kept for

Not stated by the Helpful Places.

Shared with

Not stated by the Helpful Places.

Stored

Not stated by the Helpful Places.

How to read the colours

Can it identify you?

Anonymized data
Data about people with the link to who is broken. Stripped of identifiers, blurred, aggregated, or noised so this system can’t reasonably tie a record back to an individual.
Pseudonymous data
Each person’s data is tied to a token (hash, ID, template) that lets this system recognise the same person across events, but the token itself doesn’t reveal a name. Reidentification is possible with extra information.
Identifiable data
The data either contains a direct identifier (name, address, account name, recognisable face or voice, plate number) or carries a token this system uses to look up legal identity during processing.

Who completes the loop?

Human decides
This mode suggests; a person decides what to do next. The AI is always advisory — a human is in the loop on every decision. Example: a triage tool ranks cases for a clinician who chooses which to see first.
Human executes
This mode decides; a person carries out the result. Example: an optimizer plans the day’s trash-collection routes, and drivers run them.
Autonomous
This mode decides and acts on its own. No person reviews each decision or carries out the resulting action.

Definitions from the DTPR standard. Amber is about your data, violet about who decides. The fuller the shape and the deeper the colour, the more identifying the data or the less a person is involved.

What you can do

Ask about this system

Questions go to the Helpful Places, not the vendor.

Your rights

  • Right to Algorithmic TransparencyThe use of AI in this system is disclosed to users. GC employees who use the outputs of PSSIFence are informed that an AI system is assisting in fish counting. For further information, contact Fisheries and Oceans Canada through official departmental channels.
  • Right to Be Informed of AI UseAI use is disclosed to the GC employees who are the primary users of this system. The register entry confirms 'AI use disclosed to users: Y'. As the system does not process personal information, this right applies primarily to internal government users rather than members of the public.

Risks and safeguards

  • Reputational harmInaccurate species classification or miscounts could lead to erroneous stock assessment conclusions, potentially affecting fisheries management decisions and the credibility of DFO's scientific outputs.Safeguard: The system is currently in development; GC employees serve as primary users who review AI-generated counts before they inform official assessments, maintaining human oversight. The system is intended to improve accuracy over current manual methods.