机器视觉：AI 如何看懂工厂Machine Vision: How AI Sees a Factory

Ethan, the management team saw a demo where the model detected tools and hands. They now expect the system to confirm the whole assembly process. I need to explain why those are not the same problem without sounding defensive.

Begin with the questions each layer answers. Detection identifies an object and location in a frame. Action understanding estimates what is happening over time. Process understanding connects those observations to station state, required sequence and an approved business rule.

Our first target is connector insertion. A visible connector at the end does not prove it was fully seated before the next operation. What evidence would you request?

I would review the station video, camera angle, hand occlusion, connector geometry and any available tool or PLC signals. We may need a visual state before insertion, a transition, a seated state and confirmation that the next workflow step did not begin early.

The customer keeps asking for one accuracy number. Can we quote the object-detection result from our lab?

Not as the process result. A lab detection metric does not include workflow timing, occlusion, product mix or response logic. We should define a representative validation set and report target-level results together with false alerts, missed events and review rules.

False alarms are especially sensitive because operators may stop trusting the prompt. How do we investigate them systematically?

Classify them by cause: imaging variation, target ambiguity, workflow timing, unexpected but valid behaviour, or data coverage. Then decide whether the correction belongs in lighting, camera position, process definition, model data or orchestration logic.

Would a large multimodal model reduce the need for those engineering steps?

It may improve semantic interpretation or assist review, but it does not remove the need for stable evidence, latency control and acceptance criteria. We can combine specialised models, rules and larger models according to risk rather than treating one model as universal.

The plant network is separated from the internet. Does that prevent deployment?

No. Real-time inference can run on an Edge AI computing unit inside the approved network. Models, workflow configuration and event records can remain local. Any remote access or update path must follow the customer's security process.

How should we size the edge hardware?

Use measured requirements: number and resolution of camera streams, inference frequency, model profile, preprocessing, retained video and integration workload. We should benchmark the actual pipeline and leave controlled capacity rather than select hardware from model size alone.

Management likes the phrase continuous learning. They assume every operator correction will immediately teach the model.

We should correct that expectation. Production feedback enters a reviewed queue. Engineers confirm the label and cause, update the appropriate component, run regression tests and release a version only after approval. Automatic learning from unreviewed data could amplify mistakes.

What should the pilot include so that the customer sees more than a visual demonstration?

Include representative normal and exception cases, the expected workflow state, event records, response timing, false-alert review and an agreed acceptance report. We should also document what remains out of scope, such as automatic control or unsupported variants.

Let us propose a two-stage test: first validate visual states offline, then run the workflow in observation mode at the station. The customer will provide videos, product variants and the PLC state description.

Agreed. I will provide the data checklist, imaging review and metric definitions. At the review meeting, we will present both successful cases and failure categories, then decide whether the target is ready for a controlled pilot.