When working on visual projects now, I’m often asked one question:

“Can this be done with AI?”

Yes, of course I can.

But the question is:

Not all problems require AI.

In some inspection scenarios, the rules are clear and the features are stable enough that traditional computer vision algorithms can handle them effectively.

For example:

• With or without detection;

• Dimensional measurement;

• Edge positioning;

• Simple character recognition;

• Rule-shaped judgment.

These issues, if...Stable light source, fixed position, clean backgroundUsing methods such as thresholds, edges, and template matching is actually more straightforward.

Easy to debug. Clear logic. If problems arise on-site, they’re easy to troubleshoot.

Has the light source changed? Is the threshold inappropriate? Is the positioning off? Or are the edges not properly detected?

These all have directions that can be checked.

But if you jump straight into AI right from the start, things might actually become more complicated.

You need to prepare the samples. You need to perform annotation. You need to train the model. You need to validate the results. And after going live, you’ll also need to maintain it.

Computing power, timing, and misjudgment explanations all need to be taken into account.

Finally, it might be discovered:

It’s not that AI can’t do it—it’s that this problem simply doesn’t need to be handled by AI in the first place.

Of course, AI also has scenarios where it’s particularly well-suited.

For example:

• The defect morphology varies greatly;

• The background is complex;

• There are significant differences between product batches;

• The human eye can tell, but the rules are hard to write clearly.

• Traditional algorithms have many false positives and false negatives.

In situations like these, traditional algorithms may become increasingly complex as you fine-tune them, with an ever-growing number of parameters, yet false positives and missed detections remain difficult to control.

At this point, you should seriously consider AI or adopt it.Traditional Algorithms + AIThe combination scheme.

So before selecting an algorithm, don't rush to ask:

“Should we learn AI?”

Let me start with some more practical questions:

1. Can the defect rules be described clearly?

2. Is the background stable or unstable?

3. Is the product position stable?

4. Is the defect significant?

5. Is the sample sufficient?

6. Who will maintain it later?

7. Can the production line takt time be accepted?

8. Can the customer accept the explanation for the misjudgment?

IfClear rules, stable scenario, traditional algorithms take precedence.

IfThe rules are hard to write, the changes are complex, and the sample size is sufficient.Then, carefully reconsider AI.

At the end of the day:

Machine vision is not a showcase for algorithms.

It’s not that the higher the level, the better. Rather, it’s...The better, the better.

Traditional algorithms aren't shameful. AI isn't a panacea either.

What the project site really needs isn't just a catchy concept—it's:

Run steadily over the long term.

Don't let "going AI" become the default answer for projects.

First, make sure you understand the problem clearly, then decide which tool to use.