Machinery-state detection is for visible conditions that a camera can actually distinguish. A project may need to know whether a specified machine is present in a zone, whether a protective cover appears open, whether a conveyor area contains a blockage, or whether a designated vehicle has entered a work area. Each use case needs its own visual definition.
Start with an observable condition
The first step is not to name an algorithm. It is to specify what a reviewer can see in a real image. The definition should include the target, camera position, expected normal condition, abnormal condition, minimum visible detail, and required event output. This prevents a vague request such as “monitor the machine” from becoming an untestable requirement.
Training and validation
Where a standard model does not cover the target, representative field material can be used for custom training. Training samples should include different shifts, lighting, weather, product batches, workers, equipment positions, and difficult cases. The acceptance test should use separate real-world video rather than only training examples.
Role in the control system
Video AI is useful for visual supervision and event evidence. It should be integrated alongside, not instead of, process controls, equipment interlocks, and safety procedures.