The parameters have just been adjusted.

Dozens of samples were tested, and the results remained consistently stable.

Just as I was about to breathe a sigh of relief, someone suddenly said something on the spot:

Try the next product.

As soon as the product was replaced, the system immediately began failing to detect issues or making incorrect judgments.

The engineer stares at the screen, with only one thought in mind:

wasn't everything fine just now?

The greatest fear is not improper adjustment, but repeated adjustments.

The most frustrating aspect of working on visual projects isn't being completely unable to identify elements.

Cannot identify completely; at least the issue is clear.

The most frustrating part is:

This product can be inspected; another one cannot.

This batch was ready, but the next one turned out to be different.

The white products are normal; the black products have begun to reflect light.

When the smooth-surfaced inspection tool is replaced with a rough material, the image changes again.

Finally, the scene turned into a loop:

Adjust parameters, run samples; appears normal, switch to another product; fails again.

The more engineers fine-tune the system and add more parameters, the more vulnerable it becomes.

Don't rush to blame the algorithm—first, take a look at what has changed in the product.

When a product is changed, the image often changes accordingly.

The color has changed.

Size changed.

The surface material has changed.

The level of reflection has changed.

The placement and height may also have changed.

To the human eye, it may simply be "a different model," but for a camera, these could already represent entirely distinct imaging conditions.

The exposure settings originally designed for light-colored products may result in complete blackness when applied to dark-colored items.

The detection area set for fixed dimensions may shift when changing specifications.

The originally well-tuned threshold immediately becomes ineffective when encountering new surface textures.

At this stage, persisting in optimizing a single parameter often only addresses the immediate issues of that specific product.

The next time you make a change, you'll still have to start from scratch.

The parameters are correct; the issue is that they were adjusted for only one sample.

Many visual projects have too few samples during the initial testing phase.

The engineer held a reference standard sample and repeatedly adjusted the light source, exposure settings, and algorithms.

The final result is indeed excellent.

However, this effect is likely to be applicable only to this sample.

Upon official launch, the product was available in various models and batches, with normal manufacturing variations.

If these changes are not accounted for initially, the system will become "one product with one set of parameters."

The product can still be maintained when supplies are limited.

With too many products, the scene quickly became chaotic:

The operator doesn't know which option to choose.

There are more parameter versions available.

Changing one model affects another.

In the end, even the engineers themselves dared not touch it casually.

The correct approach is not to make constant adjustments, but first clearly identify the changes.

If the replacement product fails, do not rush to adjust the threshold.

First, clarify the following questions:

How significant are the visual differences between different products?

Can size changes be achieved through positioning and dynamic area adaptation?

Can the variations in color and reflection be addressed by adjusting the lighting design?

Should different models use different formulations?

Which parameters can be shared, and which must be managed separately?

If product differences are already significant, forcing a single set of parameters to cover all models will typically fail to meet the needs of either end user group.

If only the position and dimensions exhibit regular variations, the system should automatically determine the detection area and perform automatic calculations, rather than requiring manual redrawing each time.

What a vision system truly needs is not merely "this product can be inspected now," but rather that the system continues to know how to perform inspections even after reasonable modifications are made to the product.