Get Started with Visual Inspection Toolbox
R2026bSince R2026b
Visual Inspection Toolbox™ provides apps, algorithms, and AI models for designing, testing, and deploying visual inspection and analysis applications. You can detect anomalies, count objects, match shapes, take measurements, and generate synthetic images. Combining AI models with traditional image processing helps you develop and tune inspection and analysis workflows for reliability, accuracy, and usability across applications. The toolbox, together with other MATLAB® products, reduces the time from prototyping to production to build a complete visual inspection solution. You can:
Connect machine vision cameras (with Image Acquisition Toolbox™)
Build standalone desktop dashboards and apps (with MATLAB Compiler™)
Deploy inspection algorithms to edge devices using generated code (with MATLAB Coder™ and GPU Coder™)
Export AI models to ONNX™
Tutorials
- Get Started with Visual Inspection
Learn about Visual Inspection Toolbox capabilities for automated inspection, defect detection, metrology, and object counting in industrial applications.
- Select Detection Approach for Visual Inspection
Choose among anomaly detection, shape matching, object detection, and object counting for your visual inspection application.
- Get Started with Anomaly Detection Using Deep Learning
Learn about the basics of anomaly detection.
- Train and Evaluate Anomaly Detection Model Using Visual Anomaly Detector
This example shows how to train and evaluate an anomaly detection model using the Visual Anomaly Detector app.
- Detect Anomalies Using Visual Anomaly Detector
This example shows how to detect and localize industrial production defects in images using a trained anomaly detector in the Visual Anomaly Detector app.
- Deploy Visual Inspection Code, Models, and Applications
Deploy visual inspection code, models, and applications to production environments using ONNX export, code generation, or standalone application packaging.
- Detect Objects Using YOLOX Deep Learning Network
Detect objects for visual inspection and computer vision tasks using YOLOX object detector.
Featured Examples
Localize Screen Manufacturing Defects Using Student-Teacher Anomaly Detector
Train a Student-Teacher anomaly detector on normal screen images, then localize manufacturing defects by comparing student and teacher feature outputs.
Localize Industrial Defects Using PatchCore Anomaly Detector
Perform localization of anomalous defects in printed circuit boards (PCBs) using anomaly heat maps generated with the PatchCore anomaly detector.
Detect Defects on Printed Circuit Boards Using YOLOX Network
Detect, localize, and classify defects in printed circuit boards (PCBs) using a you only look once version X (YOLOX) deep learning network.
Automate Calibrated Image Measurement Using Shape Matching
Measure objects at subpixel accuracy in calibrated camera images using shape-based matching and caliper tools.
Detect Industrial Parts Using Multi-Class Shape-Based Matching
Detect and localize multiple classes of industrial parts in search images using multi-resolution edge-based shape matching.
Generate Synthetic Training Images Using Copy-Paste Augmentation
Generate synthetic training image data store for training defect detection deep learning models.
Count Objects Using CounTR Model
Demonstrates few-shot object counting using the Counting Transformer (CounTR) model, which counts objects in an image by identifying a few exemplars without additional training.
Perform Metrology Edge Measurements and Alignment
Perform distance measurements of fill lines in soda bottles using a caliper tool, and develop a classification model for normal and anomalous fill line images.
