Separate what an image shows from what it proves
Images can reveal visible label text, a helmet or a product surface, but not every hidden property. An intact package does not establish its contents; detecting a face does not establish identity. Define a question the available image can actually answer.
Applications here are design examples, not customer lists or confirmed installations. Start with a situation resembling your work, then compare inputs, trial methods and limitations to identify conditions requiring adaptation.
For health, safety or people-related tasks, consider system responsibilities and privacy before convenience. An AI result may support review but should not become a medical diagnosis, legal conclusion or proof that a protective system works in full.
Turn an example into a reproducible trial
Define the unit being evaluated: one item, vehicle passage or reading cycle. Counting frames can count the same object repeatedly. Establish reference answers and how ambiguous observations will be adjudicated, including examples people cannot confidently interpret.
Cover normal and difficult conditions: product variants, night lighting, damaged labels and obstructions. Evaluate by condition rather than averaging away unusable situations. Record failure causes to distinguish imaging, model and workflow changes.
Before triggering alerts or business actions, assign ownership, review expectations and behavior during outages. Visual detection is only one part of the work system. Integration, control and site-specific requirements need separate confirmation.