CASE STUDYResearch · Computer vision

Applied computer-vision research

A model that is usually right is only useful in a controlled system if uncertainty changes what happens next. This work focused on measuring that boundary and deciding when the system should defer to a person.

SECTOR: Research
ROLE: Sole engineer

Detection reviewTHRESHOLD REVIEW

Above thresholdACCEPTED

Below thresholdDEFERRED

Conceptual visual based on the project's evaluation approach.

PROBLEM

A system that is usually right still needs a defensible boundary between acting automatically and handing a decision back to a person.

Raw confidence scores do not provide that boundary by themselves. They have to be evaluated against labelled examples and interpreted in the context of the cost of an incorrect decision.

SOLUTION

The work combines confidence calibration, an explicit act-or-defer threshold and a repeatable benchmark so uncertain cases have a controlled path.

01Frame receivedInput prepared for the model
02Prediction scoredA confidence score is produced
03Score calibratedCompared with labelled examples
04Act or deferThe explicit threshold controls the path

● CHECKS PASS

Above the thresholdThe result can continue through the automated path defined for the system.

● REVIEW PATH

Below the thresholdThe decision is deferred so uncertainty is visible to a person.

KEY CAPABILITIES

Confidence calibration

Confidence is checked against labelled examples.

Explicit thresholds

The act-or-defer boundary is a deliberate system decision.

Human deferral

Uncertain decisions can be returned to a person.

Reproducible benchmark

The evaluation can be repeated as the system changes.

ENGINEERING APPROACH

A score is useful only when its meaning is measured.

The published material covers the confirmed evaluation approach without claiming a dataset size, model result, deployment outcome or commercial impact.

Labelled evaluation

Confidence is checked against known examples.

Calibration

Raw scores are not assumed to be reliable probabilities.

Explicit threshold

The act-or-defer boundary is selected deliberately.

Benchmarking

The same evaluation can be reproduced later.

OUTCOME

The work turns model uncertainty into an operational decision: results can follow the automated path only when they meet the chosen threshold; the rest are deferred.