Industrial Vision: From 99% to 99.99%, Where Is the Difficulty?

Many people think the hardest part of industrial vision is getting the recognition rate to 99%.
But anyone who has truly worked on projects knows.
99% may just be the beginning.
Going from 99% to 99.9% may be ten times harder.
Going from 99.9% to 99.99% is sometimes harder than going from 0 to 99%.

Why?

Because the biggest enemy of industrial vision has never been the algorithm.

It is the extreme situations you never anticipated.

For example.
A part inspection project.
1,000 samples tested in the lab.
99% recognition rate.
It already looks very good.
But if the factory produces 100,000 products a day.
What does a 1% misjudgement rate mean?
It means 1,000 problematic pieces every day.
Customers simply dare not put such a system into production.

So for many industrial scenarios.
99% is not enough.
99.9% may not be enough either.
In some new energy, semiconductor, and automotive parts industries.
What customers require is often 99.99%, or even higher.

01Where Exactly Is the Difficulty in That Remaining 0.99%?

Here comes the question.
Where exactly is the difficulty in that remaining 0.99%?

Many times, it is not that the model is not smart enough.
But that the site is too complex.
For example, changes in lighting.
Parameters adjusted in the morning.
When the sunlight changes in the afternoon, the results are different.
Another example is reflection.
The same product viewed from a different angle, and the defect suddenly becomes invisible.
There are also stains, dust, vibration, product deformation, and material batch variations.
These problems may rarely occur in the lab.
But they happen every day in a real factory.

And what truly determines the success or failure of a project is often these long-tail scenarios.

02The First 80% Develops Very Fast; the Last 20% Consumes 80% of the Time

So you will notice a phenomenon.
In many industrial vision projects.
The first 80% of development progresses very quickly.
The last 20% of optimisation may consume 80% of the time.
Customers keep raising new problems.
Engineers keep patching loopholes.
Algorithms keep iterating.
In the end, everyone is wrestling with those few extreme samples.

This is also why many people feel:
industrial vision is becoming increasingly difficult to do.
Because the problems that are easy to automate have already been solved for the most part.

What remains are the hard nuts to crack.

03The Future Competes Not on Accuracy, but on Robustness

So what is the way forward?

In recent years, a very obvious trend has emerged.
In the past, everyone pursued higher model accuracy.
In the future, everyone will pay more attention to system robustness.

What is robustness?

It is not getting all 1,000 samples right in the lab.It is running continuously in the factory for a year.Facing changes in lighting, equipment ageing, and product fluctuations, and still working stably.

Because what customers truly buy has never been an algorithm.
But rather a stable production capability.

04What Determines the Gap Between 99% and 99.99% Is Often Not the Code

So the most valuable people in the industrial vision industry in the future may not necessarily be those with the strongest algorithms.

But those who best understand the site.

Because what determines the gap between 99% and 99.99%.
Is often not the code.
But rather the comprehensive capability of processes, equipment, optics, mechanics, and on-site experience.

The biggest challenge of industrial vision has never been seeing problems.

But rather, in the complex real world, seeing problems continuously and stably.

And this is precisely where the greatest value of industrial vision lies.