Direct answer
Run a documented feasibility study on real good and defective samples before committing, and specify performance as detection and false-reject rates on that sample set.
What this discipline delivers
- Camera, lens and resolution selection against feature size
- Lighting geometry and wavelength choice
- Fixturing and presentation repeatability
- Algorithm approach: rule-based, deep learning or hybrid
- Validation protocol and periodic verification
Method and stages
Feasibility study
Imaging trials on real samples producing evidence, not assurances.
Rule-based inspection
Deterministic, explainable, straightforward to validate.
Deep-learning inspection
For variable-appearance defects; needs labelled data and retraining governance.
3D imaging
Where the defect or feature is geometric rather than tonal.
Engineering and project considerations
- Ambient light must be excluded or controlled
- Feature resolution: several pixels across the smallest defect, not one
- Sample sets must include marginal cases, not only clear defects
- Deep-learning models require version control and change validation
- Result storage for traceability and post-event analysis
What to prepare before engaging engineering companies
- Physical samples: good, marginal, defective
- Defect definitions with accept/reject boundaries
- Line speed and presentation conditions
- Validation and audit requirements
What to measure
Frequently asked questions
Can vision be specified without samples?
Not credibly. Any supplier quoting performance without imaging trials on your product is quoting an assumption.
Related engineering knowledge
Independent, buyer-side and supplier-neutral
Global B2B Group does not sell machines and does not represent equipment manufacturers. This material is published to help industrial organisations define the problem, prepare the specification and structure the investment before engineering partners are selected.