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Automated Visual Inspection

Automate quality assurance tasks using anomaly detection and classification techniques

Automated visual inspection (AVI) is a set of techniques used to determine whether an image represents a normal ("good") state or an anomalous ("defective") state. AVI assists and improves quality assurance processes commonly found in manufacturing settings. Modern visual inspection uses machine learning and deep learning techniques to produce useful results.

The specific technique you select to automate a visual inspection task depends on several factors. These factors include the amount of training data available for normal and anomalous samples, the number of anomaly classes to recognize, and the type of localization information required for understanding and monitoring predictions.

To perform automated visual inspection, download the Computer Vision Toolbox™ Automated Visual Inspection Library from the Add-On Explorer. For more information on downloading add-ons, see Get and Manage Add-Ons. Some functionality also requires Deep Learning Toolbox™.


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groundTruthGround truth label data
imageDatastoreDatastore for image data
sceneLabelTrainingDataCreate training data for scene classification from ground truth
splitAnomalyDataSplit data into training, validation and testing sets for anomaly detection
trainFCDDAnomalyDetectorTrain fully convolutional data description (FCDD) anomaly detection network
trainFastFlowAnomalyDetectorTrain FastFlow anomaly detection network
trainPatchCoreAnomalyDetectorTrain PatchCore anomaly detection network
anomalyThresholdOptimal anomaly threshold for set of anomaly scores and corresponding labels
fcddAnomalyDetectorDetect anomalies using fully convolutional data description (FCDD) network for anomaly detection
fastFlowAnomalyDetectorDetect anomalies using FastFlow network
patchCoreAnomalyDetectorDetect anomalies using PatchCore network
classifyClassify image as normal or anomalous
predictPredict unnormalized anomaly scores
anomalyMapPredict per-pixel anomaly score map
anomalyMapOverlayOverlay heatmap on image using per-pixel anomaly scores
viewAnomalyDetectionResultsView anomaly detection results
evaluateAnomalyDetectionEvaluate anomaly detection results against ground truth
anomalyDetectionMetricsAnomaly detection metrics