General-purpose vision is getting cheaper, while high-end vision is leveling up.
Industrial vision lacks not people who can do vision but those who can make it work. Generic vision—OCR, QR codes, object detection—is standardized, competing on price and delivery speed. High-end scenarios demand 0.3mm accuracy at 600m, 0.1mm impurity detection, or high-speed ore recognition; the hard part isn't the model but vibration, thermal deformation, distortion, light consistency and proce
What is truly lacking is not people who can do vision.
But rather people who can truly "make it work."
The previous short video on industrial vision generated strong reactions,
and many people came to consult about projects with specific scenarios.
As a result, a particularly interesting phenomenon emerged.
The projects that are truly willing to pay are all extremely difficult.
"Measure a dimension at a distance of 600 metres with precision down to 0.3 mm."
"Identify impurities of 0.1 mm."
"Identify different ores under high-speed motion."
At this point you will find:
industrial vision is not "commonplace" at all.
What is truly commonplace is only "general-purpose vision."
For example: OCR, QR codes, object detection, personnel recognition.
These capabilities have become increasingly standardised today.
Open-source models are mature;
training pipelines are mature;
deployment solutions are mature.
Many projects no longer even compete on algorithms.
But rather on: who is cheaper, who delivers faster.
But once you enter high-end industrial scenarios, the entire rule of the game changes immediately.
Because what is truly difficult for customers has never been: can it see.
But rather: can it see accurately and stably.
01"Stability" – These Two Words Are the Most Expensive Thing in Industry
Note, "stability" – these two words – are the most expensive thing in industry.
Take 0.3 mm precision, for example.
Many people's first reaction is: is the model not strong enough?
In fact, in many cases, AI is not the core contradiction at all.
What is truly difficult is: equipment vibration; thermal deformation; lens distortion; motion blur; light source consistency; repeat positioning accuracy.
Even a few degrees of temperature change in the workshop can cause the results to drift.
Another example is 0.1 mm impurity detection.
In many scenarios:
the impurity and the background are almost the same colour; the surface is reflective; the material itself has texture noise.
At this point, what is being competed on is no longer "can you use YOLO."
But rather:
Do you understand the process?
02Once Industrial Vision Enters Deep Water, It Increasingly Resembles Something Other Than the AI Industry
You will find that once industrial vision enters deep water, it increasingly resembles something other than the AI industry.
It is more like:
optics, control, mechanics, process, software, algorithms –together forming a set of industrial precision systems.
Truly excellent projects are ultimately often not solved by a single model.
But rather by:
how the camera is mounted;how the light is directed;how the PLC is linked;how the motion takt is synchronised;how real-time processing is done at the edge;how abnormal states are handled with fallbacks.
It is all of these things combined that constitute the true barrier.
03The Future Industrial Vision Industry May Become Increasingly Polarised
So the future industrial vision industry may become increasingly polarised.
Low-end general-purpose vision will become cheaper and cheaper, because it is becoming infrastructure.But high-end vision will have ever-higher barriers, because it begins to demand: cross-disciplinary capability.
You not only have to understand algorithms. You also have to understand the industrial site.
And the further you go, the more clearly you will feel:
What is truly valuable is not "being able to tune models."But rather: who truly understands the production process.