Visual Inspection with MATLAB
Build machine vision inspection systems using Visual Inspection Toolbox. Select the
right approach — rule-based, AI-based, or hybrid — based on problem characteristics,
then implement, train, and deploy.
When to Use
Defect detection and classification
- Detecting anomalies on manufactured parts (scratches, dents, contamination)
- Training anomaly detectors with good-part images only (one-class learning)
- Detecting and localizing defects with bounding boxes (YOLOX)
- Classifying defect types or severity levels
Measurement and gauging
- Measuring widths, gaps, angles, diameters on parts
- Calibrated measurement in physical units (mm, degrees)
- Vision fixturing — locating parts and repositioning measurement tools
- Blob analysis — area, perimeter, shape metrics of segmented regions
Counting and localization
- Counting identical rigid parts via shape matching
- Few-shot counting with intra-class variation (CounTR)
- Locating parts by edge geometry for downstream inspection
Training data augmentation
- Generating synthetic labeled data via copy-paste augmentation
- Expanding small defect datasets for YOLOX training
Deployment
- Packaging inspection as standalone executables (MATLAB Compiler)
- Generating C/C++ for embedded/edge targets (MATLAB Coder)
- GPU-accelerated inference with TensorRT (GPU Coder)
- ONNX export for NPUs, AI accelerators, smart camera SDKs
- Deploying to Beckhoff TwinCAT industrial PLCs
When NOT to Use
- Camera acquisition, frame grabber configuration — use Image Acquisition Toolbox
- PLC communication, OPC UA, MQTT protocols — use Industrial Communication Toolbox
- General image processing unrelated to inspection (artistic filters, medical imaging)
Must-Follow Rules
Approach Selection
- Rule-based for geometric, deterministic problems — when defects are dimensional deviations or parts have known geometry, use caliper/fitcircle/matchshape. No training data needed
- AI-based for variable appearance — when defect appearance can't be enumerated, use anomaly detection or YOLOX. Requires training data
- Hybrid when parts move — use
matchshape to fixture (locate + normalize pose), then AI on the cropped/warped region. Anomaly detectors are NOT rotation/scale invariant
Anomaly Detection
- Anomaly detectors require consistent pose — CNN backbones are not rotation/scale invariant. Fixture with
matchshape + imwarp to canonical orientation before training and inference
- Choose detector by data availability — >100 good images:
studentTeacherAnomalyDetector; <100 images: patchCoreAnomalyDetector; have anomaly labels: fcddAnomalyDetector
- Use
classify for inference — returns [tf, score, map] for anomaly map visualization
Object Detection (YOLOX)
- Use
AutoResize=false for full-resolution inference — prevents downscaling that loses small defects. Pair with tiled training when objects are small relative to image
- Synthetic data benefits YOLOX most — copy-paste augmentation via
objectInsertionDatastore is proven for detection tasks
Measurement
- Vision fixturing pattern —
matchshape locates part → transform measurement positions to detected pose → measure at repositioned locations
- Calibrated measurements require
imageToWorldPlane — converts pixel measurements to physical units using camera calibration
images.geotrans.Warper for production rectification — precompute the mapping once, apply per frame for maximum throughput
Deployment
- Always use
persistent + isempty guard in codegen entry points — load model once, infer many times
coder.loadDeepLearningNetwork for DL models — not coder.load (which is for structs/shape models)coder.Constant for MAT file path — codegen requires compile-time constant paths- Pass detector objects directly to
exportONNXNetwork — never extract .Network or .Backbone first. The object-level export includes pre/post-processing logic
- ONNX for NPUs/AI accelerators; GPU Coder + TensorRT for NVIDIA — don't use ONNX for Jetson/Orin
Pipeline Construction
Machine vision inspections compose tools in sequence:
- Acquire — Get the image (Image Acquisition Toolbox)
- Fixture — Locate a stable reference feature (
matchshape)
- Reposition — Transform measurement positions to detected pose
- Inspect — Apply measurement or classification at repositioned locations
- Decide — Compare to tolerances → pass/fail
- Communicate — Send results to PLC/SCADA (Industrial Communication Toolbox)
Common Hybrid Patterns
| Pattern |
Rule-Based Part |
AI Part |
| Fixture + classify |
matchshape locates part, crops ROI |
Anomaly detector on cropped region |
| Fixture + detect |
matchshape locates part |
YOLOX finds defects relative to fixture |
| Pose normalize + anomaly |
matchshape + imwarp to canonical pose |
Anomaly detector on normalized image |
| Segment + measure |
Thresholding / morphology |
regionprops on segmented blobs |
Primary Decision: Rule-Based vs. AI-Based
| Choose Rule-Based When |
Choose AI-Based When |
| Defects have predictable, enumerable geometry |
Defect appearance varies unpredictably |
| Lighting and positioning are tightly controlled |
Background or part appearance varies |
| Need dimensional measurements (mm, degrees) |
Need classification without precise measurement |
| Few or no labeled examples available |
Have labeled training data (or can generate synthetic) |
| Require explainable, auditable decisions |
Accuracy matters more than explainability |
Key Functions
| Function |
Purpose |
Toolbox |
Available From |
shapemodel |
Build edge-geometry model from template |
Visual Inspection Toolbox |
R2026b |
matchshape |
Find model instances in search image |
Visual Inspection Toolbox |
R2026b |
caliper |
Measure edge distances along a profile |
Visual Inspection Toolbox |
R2026b |
fitangle |
Measure angle between edges |
Visual Inspection Toolbox |
R2026b |
fitcircle |
Measure circle center and radius |
Visual Inspection Toolbox |
R2026b |
studentTeacherAnomalyDetector |
One-class anomaly detection (>100 good images) |
Visual Inspection Toolbox |
R2026b |
patchCoreAnomalyDetector |
One-class anomaly detection (few-shot, <100 images) |
Visual Inspection Toolbox |
R2026b |
fastFlowAnomalyDetector |
Normalizing-flow anomaly detection |
Visual Inspection Toolbox |
R2026b |
fcddAnomalyDetector |
Semi-supervised anomaly detection |
Visual Inspection Toolbox |
R2026b |
yoloxObjectDetector |
Object/defect detection with bounding boxes |
Visual Inspection Toolbox |
R2026b |
counTRObjectCounter |
Counting with intra-class variation (few-shot) |
Visual Inspection Toolbox |
R2026b |
objectInsertionDatastore |
Synthetic labeled training data (copy-paste) |
Visual Inspection Toolbox |
R2026b |
regionprops |
Blob area, perimeter, shape features |
Image Processing Toolbox |
R2006a |
imageToWorldPlane |
Calibrated world-unit measurement |
Computer Vision Toolbox |
R2022b |
Decision Trees
"I need to detect defects"
| Situation |
Approach |
| Defect appearance unpredictable; good-part images only |
Anomaly detection |
| Classify defect types with bounding boxes; have labeled data |
YOLOX object detection |
| Defects are geometric deviations from known shape |
Shape matching + gauging |
| Dimensional out-of-tolerance (too wide, too narrow) |
Caliper / fitcircle / fitangle |
"I need to measure parts"
| Situation |
Approach |
| Widths, gaps, edge-to-edge distances |
caliper |
| Angles between edges |
fitangle |
| Hole diameters, arc radii |
fitcircle |
| Area, perimeter, shape of segmented blobs |
regionprops |
| Need measurements in mm (physical units) |
Camera calibration + rectification |
| Part position varies image-to-image |
Vision fixturing with matchshape |
"I need to count objects"
| Situation |
Approach |
| Identical rigid parts, controlled background |
shapemodel / matchshape |
| Intra-class variation, few labeled examples |
CounTR (few-shot counting) |
| Multiple classes, complex scenes, have labels |
YOLOX detection → count |
"I need to locate parts"
| Situation |
Approach |
| Rigid part, known edge geometry |
shapemodel / matchshape |
| Variable appearance, multiple classes |
YOLOX detection |
"I don't have enough training data"
| Situation |
Approach |
| Have segmented defect examples + clean images |
Copy-paste synthetic data |
| Good-part images only (no defect labels) |
Anomaly detection (one-class) |
| No data at all, defects are geometric |
Rule-based (no training needed) |
"I need to deploy"
| Target |
Path |
| Standalone app, full MATLAB features |
MATLAB Compiler |
| Monitoring dashboard or UI app |
MATLAB Compiler |
| Embedded CPU (x86, ARM) |
MATLAB Coder |
| NVIDIA Jetson/Orin or dGPU |
GPU Coder + TensorRT |
| NPU or AI accelerator |
ONNX export |
| Smart camera with vendor SDK |
ONNX export |
| Beckhoff industrial PLC |
MATLAB Coder → TwinCAT |
Patterns
Each reference file contains executable code patterns, API calling conventions, and
worked examples for its topic. Load the relevant reference before writing code.
Conventions
- Always: use machine vision terminology (rule-based, tool chaining, fixturing)
- Always: recommend the specific VIT function, not generic IPT building blocks
- Always: consider hybrid pipelines (rule-based fixturing + AI classification)
- Always: ensure anomaly detector receives images in consistent pose
- Always: use
persistent + isempty for model loading in codegen entry points
- Always: pass detector objects directly to
exportONNXNetwork (never extract .Network)
- Never: recommend AI when the problem is purely dimensional measurement
- Never: recommend rule-based when defect appearance is highly variable
- Never: use
coder.load for deep learning models (use coder.loadDeepLearningNetwork)
- Never: use ONNX export for NVIDIA targets (use GPU Coder + TensorRT)
- Prefer: anomaly detection when only good-part images are available
- Prefer: YOLOX when labeled defect examples with bounding boxes exist
- Prefer:
shapemodel/matchshape over YOLOX for locating rigid parts
- Prefer:
images.geotrans.Warper for production rectification throughput
- Prefer: MATLAB Compiler for dashboards and UI-based monitoring apps
References
| Load when... |
Reference |
| Training or using anomaly detectors |
references/detect-anomaly.md |
| Locating parts by shape, vision fixturing |
references/match-shape.md |
| Training or using YOLOX object detectors |
references/detect-objects.md |
| Counting objects (shape matching, CounTR, YOLOX) |
references/count-objects.md |
| Measuring with caliper, fitcircle, fitangle |
references/measure-gauging.md |
| Blob analysis, regionprops, calibrated measurement |
references/blob-analysis.md |
| Deploying models (Compiler, Coder, TensorRT, ONNX) |
references/deploy-inspection.md |
| Generating synthetic training data |
references/synthetic-data.md |
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