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name: matlab-use-visual-inspection description: > Build machine vision inspection systems with MATLAB Visual Inspection Toolbox. Covers the full arc from problem selection through deployment: anomaly detection (Student-Teacher, PatchCore, FastFlow, FCDD), object detection (YOLOX), counting (CounTR, shape matching), gauging and measurement (caliper, fitcircle, fitangle), blob analysis (regionprops, calibrated measurement), shape matching for fixturing and localization, synthetic data generation for training augmentation, and deployment to standalone apps, embedded C/C++, NVIDIA GPUs (TensorRT), NPUs (ONNX), and Beckhoff PLCs. Routes to rule-based tools when problems are geometric and deterministic, AI-based tools when defect appearance varies, and hybrid pipelines combining both. license: "https://www.mathworks.com/content/dam/mathworks/license/pmrl/license.md" metadata: author: MathWorks version: "1.1"

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:

1. Acquire — Get the image (Image Acquisition Toolbox)
2. Fixture — Locate a stable reference feature (matchshape)
3. Reposition — Transform measurement positions to detected pose
4. Inspect — Apply measurement or classification at repositioned locations
5. Decide — Compare to tolerances → pass/fail
6. 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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