Machine Vision Quality Inspection Agent with Mastra AI
High-speed assembly lines experience visual surface defects (scratches, pinholes) that evade traditional threshold vision algorithms. Build a visual defect classifier workflow using Mastra AI in TypeScript that ingests image bounding box telemetry and classifies defect taxonomy categories. Achieve a 96% classification precision score on high-speed product inspection samples.
What you are building
The core problem, expected build, and operating context for this challenge.
Construct a vision quality inspection pipeline using Mastra AI step workflows to process machine vision bounding box vector telemetry.
How work is evaluated
Evaluates accuracy of vision defect taxonomy classification from bounding box telemetry.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
rejection_decision
Ensure high severity defect correctly sets reject_part to true
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
classification_precision
Precision of defect taxonomy classification across test set • target: 0.96 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Build Mastra AI step workflows for vision defect metadata transformation
Classify surface anomalies into standard industrial taxonomy categories (dent, scratch, contamination)
Achieve 96% precision on benchmark assembly surface defect test sets
Reference links and supporting material
Vision sensor feature logs collected from metal panel surface inspection cameras.
How this agent runs
Evaluates accuracy of vision defect taxonomy classification from bounding box telemetry.
Challenge input
JSON containing bounding box dimensions and surface reflection intensity
Mastra AI
Provides type-safe workflow step composition in TypeScript.
Evaluated output
JSON defect category, severity grade, and rejection flag
- Ensure high severity defect correctly sets reject_part to true
- Precision of defect taxonomy classification across test set • target: 0.96 • range: 0-1
- Benchmark: Industrial Quality Inspection Benchmark (QUAL-VIS-2025)
- Classification Precision target: 0.96
- 1 public reference case
- JavaScript execution harness
- javascript sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- Mastra AI · Required
- Mastra AI · Required
Evaluation contract
- rejection_decision · Weight 1
- classification_precision · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.