Computer vision is at its best when a task is visual, repetitive, and currently limited by human attention. It struggles when the visual signal itself is weak or ambiguous.
Where vision reliably wins
High-volume inspection, presence and absence checks, reading text and codes, counting, and basic safety monitoring are all settings where a model outperforms tired human eyes, consistently and around the clock.
These tasks share a trait: the right answer is visible and well defined, even if the volume makes humans unreliable.
Where it struggles
Subtle judgement calls, scenes with poor or wildly varying lighting, and rare events with almost no training examples are where vision projects stall. None are impossible, but all need honesty about cost and accuracy.
If a skilled human disagrees with another skilled human, do not expect a model to settle it cleanly.
Make it real
The hard part is rarely the model. It is lighting, mounting, edge cases, and the pipeline that turns a prediction into an action your systems trust. Design for the messy real world and the accuracy follows.
Use vision for visual, repetitive, attention-limited work. Be cautious where the signal is weak, and budget for the real-world plumbing.
Working on something like this?
We are happy to share specific, relevant examples privately, no pitch.
