When to Review AI Visual Inspection Results

A camera captures a thin, bright line on a painted panel. It might be a scratch or glare from a light above. If the image does not support a reliable decision, the inspection process may route it to a person for review.

A review request does not by itself mean the inspection has failed. It can be one planned outcome in a process that defines which results require a person, what the reviewer checks, and what happens to the part while a decision is pending. This article explains that process and its limits.

Why the AI sends an image to a person

An inspection system may return a result and, in some implementations, a confidence score. Research on model calibration shows that confidence estimates need not match correctness automatically (Guo et al., 2017), so validate any review rule on representative parts. Define which results require review and what happens while the decision is pending.

Being unsure is only one reason. An image may also go to a person when:

The picture is bad. Glare, blur or a part in the wrong place can hide what needs to be seen.
The part looks new. AI gets worse when parts don’t look like the images it learned from, for example after a change in product type, worn tools or new lighting.
A mistake would be serious. Some checks always go to a person because a wrong answer would cost a lot or be unsafe.
It’s a spot check. Teams also recheck some results the AI was sure about, because how sure a model seems doesn’t always match how often it’s right.

1AI returns a result. Pass, fail, or a result that needs review.

2Does a review trigger apply? Unsure result, bad image, new-looking part, high-risk check or scheduled spot check.

No →Result stands. The part continues as normal.

Yes ↓Send for review. Continue with steps 3 to 5.

3Hold the part. Follow the defined pending path so the part does not move on unreviewed.

4A person reviews. Check the image and, if needed, the part itself against the written rules.

5Decide and record. Pass, fail, retake the image or escalate, with a short reason.

Figure 1: A simple review workflow. Real steps differ by plant.

What the person should check

If your plant has its own review steps, use those first. If not, this order works:

Can you see the problem area? If glare or blur hides it, don’t guess. Take a new image or look at the part.
Is it the right part? Check the part number, product type and work order.
What do you see? Decide for yourself before you rely on what the AI suggests. Look at more than just the area it marked.
What do the written rules say? Compare the mark’s type, size and location with your written rules or approved samples. A Sandia review discusses how inspection performance depends on clear criteria and consistent procedures (J. E. See, 2012).
Is the image enough? If not, go to the part. Look at it from another angle or measure the mark.
Should someone else decide? Ask for help if the rules don’t cover it, safety is involved or the same problem keeps coming back.
Write it down. Record your decision and a short reason: “real defect,” “false alarm,” “bad image” or “rules not clear.”

How human review helps

First, a guess doesn’t become a final decision. Someone takes charge of the result, and they can do what a camera can’t, like tilting the part or knowing that a process changed this morning.

Records can help teams identify recurring issues and may inform a separately governed model update, for example when teams use reviewed examples in a controlled learning process (Bauer et al., 2025). Treat them as evidence for investigation; a review decision does not change a model automatically.

Records can also show that problems keep coming from one camera, shift or batch of material. They help set the limits as well. Sending more images to people means more work for them. Sending fewer means you rely more on the AI. Count missed defects and false alarms separately to see which way to go.

Example: scratch or glare?

This example is made up to explain the idea.

Back to the painted panel. The image shows a bright band around the line, so the technician goes to the panel and tilts it under a light. The line disappears from other angles and the surface has no mark. They pass the panel and write “false alarm: reflection.” If the line had stayed, they would have checked it against the written limit.

A week later, the records show similar results from the same station, mostly in the afternoon. An engineer finds daylight from a nearby door hitting the panels and blocks it. The AI never needed changing.

Limitations

Review only covers what reaches a person. If the AI wrongly passes a defect and is sure about it, nobody sees it unless a spot check catches it.

People make mistakes too. Visual inspection has limitations, and research on automation use describes risks such as missing events the system does not flag or following incorrect automation advice. These are reasons to design review procedures and oversight carefully, not to assume that human review guarantees detection (J. E. See, 2012; Parasuraman and Manzey, 2010).

A person also can’t see what isn’t in the image. If images keep coming out bad, fix the camera, the lighting or the way the part is placed. For more on capture quality, see why high-speed defect detection starts with a better image. Too many images for review just create a waiting line, so decide early what happens to a part while it waits.

Human review also doesn’t replace your quality process. Your plant’s rules and any customer or legal requirements still decide what passes and who approves it.

Conclusion

Send an image to a person when the AI is unsure, the picture is bad, the part looks new, a mistake would be serious or a spot check is due. Then check the image, compare it with the written rules, look at the part if needed and write down why. Those notes help the whole inspection get better.