How do you calculate recall for an AI inspection model?

Divide the number of defects the model correctly detected by the total number of actual defects in the validation set, then multiply by 100. With 96 caught out of 100 real defects, recall is 96%.

What is a good recall for defect detection?

For safety-critical inspection, teams often demand 98-99.5% recall because each missed defect is a customer escape. A 96% recall against a 98% spec leaves a 2-point gap to close before deployment.

What does the gap to specification mean?

It is how many percentage points short of the required recall the model currently sits. Here the model is at 96% against a 98% requirement, a 2-point gap, meaning roughly 2 in 100 real defects still escape.

Precision vs recall in machine vision inspection?

Recall measures missed defects (escapes); precision measures false alarms (good parts wrongly rejected). Inspection usually weights recall higher because escapes reach customers, but very low precision causes costly over-rejection and operator fatigue.

How do I improve recall without hurting precision?

Add more labeled examples of the missed defect types, rebalance the training set, or lower the decision threshold while watching precision. Often the cleanest fix is targeted data collection on the specific defects the model misses.

How big should the validation sample be?

Large enough that the defect count gives stable statistics; 100 actual defects yields recall to about 1-point resolution, but safety-critical claims of 99%+ recall need several hundred defects to be credible.

Machine Vision & Industrial Inspection AI calculator

AI Inspection Precision and Recall Calculator

Recall is the share of real defects an AI inspection model actually catches, computed as true positives divided by the total defects present in a labeled validation sample. In quality inspection it is the metric that matters most for safety, because a missed defect (low recall) ships a bad part to the customer. ML engineers and quality leads track recall against a contractual or internal spec to decide whether a model is ready to deploy or needs more training data. This calculator returns model recall as a percentage and the gap in points to your required detection specification.

What this calculator does

Calculate the recall (sensitivity) of an AI inspection model from a validation sample by dividing true positives by the total number of actual defects, and see how far the result is from your required detection specification.
Use it when validating an AI inspection model against a known-defect sample set and you need to calculate and document detection recall before releasing the model to production.
It computes detection recall as true positives over total actual defects, and the gap in percentage points to your required recall specification.

Formula used

Recall = true positives / total actual defects x 100
Gap to specification = required recall - current recall

Inputs explained

True positives (defects correctly detected):
Total actual defects in validation sample:
Required detection recall specification:

How to use the result

Use it when validating or auditing an AI defect-detection model against an escape-rate target before or during production deployment.
Recall alone ignores false positives; a model that flags everything reaches 100% recall while overwhelming operators, so always read it alongside precision.

Common questions

How do you calculate recall for an AI inspection model? Divide the number of defects the model correctly detected by the total number of actual defects in the validation set, then multiply by 100. With 96 caught out of 100 real defects, recall is 96%.
What is a good recall for defect detection? For safety-critical inspection, teams often demand 98-99.5% recall because each missed defect is a customer escape. A 96% recall against a 98% spec leaves a 2-point gap to close before deployment.
What does the gap to specification mean? It is how many percentage points short of the required recall the model currently sits. Here the model is at 96% against a 98% requirement, a 2-point gap, meaning roughly 2 in 100 real defects still escape.
Precision vs recall in machine vision inspection? Recall measures missed defects (escapes); precision measures false alarms (good parts wrongly rejected). Inspection usually weights recall higher because escapes reach customers, but very low precision causes costly over-rejection and operator fatigue.
How do I improve recall without hurting precision? Add more labeled examples of the missed defect types, rebalance the training set, or lower the decision threshold while watching precision. Often the cleanest fix is targeted data collection on the specific defects the model misses.

Last reviewed 2026-05-12.