Unknown-Anomaly and Few-Shot Defect Detection

Scope and project-specific requirements

Critical decision conditions

Confirm that normal training samples are uncontaminated and reserve real defects from independent batches; an anomaly score is not a defect-class probability.

Candidate technical approach

These are candidate implementation paths. Establish a baseline on real samples, then select or combine methods for imaging, speed and deployment constraints. Model names do not imply measured project results.

Inputs for assessment

Provide product variants, defect definitions and reference parts, acceptable and borderline samples, field of view, takt time, cameras/lighting and trigger/reject interfaces.

Deliverables and interface agreement

Anomaly model, threshold configuration, heatmaps and borderline-sample list

Specify input formats, output fields, coordinates/units, error states, versions and invocation methods. Define review and failure handling. The statement of work determines the exact scope of source code, executables, model files or analysis reports.

Acceptance method and measures

Separate test data by product, batch and defect class. Report misses, false calls, minimum visible conditions and line cycle time; test physical rejection separately.

These are measures to agree and test, not achieved-performance claims. Freeze samples, reference truth, thresholds and hardware/software versions before acceptance; report subgroup results and failures, identifying under-sampled conditions as uncovered.

Implementation and procurement stages

1. Define scope

   Agree targets, stations, inputs and responsibilities in a statement of work, separating required and excluded conditions.

2. Sample validation

   Use representative samples to test critical risks, document feasibility/failures and scope the next-stage estimate.

3. Development and integration

   Implement agreed functions and interfaces with configuration/change records; use offline replay before authorized device or site integration.

4. Acceptance and handover

   Retest against individual measures and hand over contracted artifacts with known limits; manage maintenance, expansion and changeovers as subsequent work packages.

Scope limits and licensing

Image decisions cover visible features under agreed conditions; acceptable appearance does not establish material, strength, sealing or electrical conformity.

Candidate technologies are not license clearance. Check code, model weights, training data and dependency versions separately. Replace, license or exclude components unsuitable for the intended commercial delivery. Customer data is not used for public training by default.

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