Pilot Plan

Vision Inspection Pilot Spec & Acceptance Criteria (Interactive Pilot Template)

An interactive pilot-planning template to scope, run, evaluate, and decide on machine-vision inspection pilots. Includes data, metric, integration, and operator-acceptance fields plus guidance.

Interactive Tool

Vision Inspection Pilot Template

Use this form to plan and record a vision inspection pilot. Complete the fields to define scope, data needs, acceptance criteria, integration points, and evaluation steps. Save the plan so the team can review, iterate, and run the pilot with clear success criteria.

Quick guidance: aim for hundreds of varied labeled images per defect class for a feasibility pilot (200–500); for production-ready models, 1,000+ per class is common when practical. Prefer consistent, controlled lighting and fixed camera mounts during the pilot. Use the help text for each field for practical tips.

Short descriptive name (e.g., 'Front Panel Solder Joint Inspection - Pilot').
What are you testing? Be specific about defects, expected impact (quality/time), and hypothesis (e.g., 'Vision can detect missing screws with >=90% precision at line speed').
List defect classes and show example conditions (e.g., 'missing screw, cracked seal, label wrinkle'). Include acceptable vs unacceptable variants where relevant.
Total images to collect for the pilot. Guidance: 200–500 per defect class for feasibility testing; 1,000+ per class for robust production models if practical.
Describe camera placement, resolution, fixed mounts, lighting setups, backgrounds, required metadata (timestamp, lot, operator), and naming conventions. Prefer reproducible, fixed setups during the pilot to reduce variance.
Who will label images, what labels will they apply, how will you verify label quality (e.g., second rater, spot checks), and which tool will you use?
Choose a metric suited to your defect prevalence and business cost of false positives vs false negatives. F1 or precision/recall are often preferred for imbalanced classes.
Numeric threshold for the selected metric (enter as percent, e.g., 90 for 90%).
Useful when false positives disrupt operations or create rework.
Useful when missing defects would cause safety, regulatory, or major quality issues.
An operational metric that limits alarm fatigue and unnecessary checks.
Consider latency, bandwidth, privacy, maintainability, and IT/OT constraints.
How many parts per minute must the solution handle without creating a line bottleneck?
How will alerts be presented (HMI, light tower, SCADA, mobile)? Who responds, what corrective steps exist, and how will the operator confirm or override an alert? Describe any PLC or MES interactions.
How will false positives/negatives be captured, reviewed, and incorporated into retraining? Who owns the data pipeline and retraining cadence?
Time to collect data, run the model in test mode, and evaluate performance across shifts and part lots.
Describe test/train/holdout splits, offline vs online evaluation, key reports, and planned review meetings. Include how operator feedback will be recorded.
Qualitative measures such as reduced inspection time, trust in alerts, usability of UI, and impact on workload.
List likely failure modes (lighting changes, part variability, occlusion, label errors) and planned mitigations (shielding, additional samples, augmented data, fallback manual inspection).
Select the intended outcome after reviewing results against metrics and operator feedback.
List names, roles (process owner, ML engineer, QA lead), and contact info for decision-making and execution.
Any other context worth recording (regulatory constraints, customer requirements, shift schedules, spare cameras).
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