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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™ from the add-on explorer. for more information on downloading add-ons, see . some functionality also requires deep learning toolbox™.

functions

load training data

	ground truth label data
	datastore for image data
	create training data for scene classification from ground truth
	split data into training, validation and testing sets for anomaly detection

train anomaly detector

	train fully convolutional data description (fcdd) anomaly detection network
	train fastflow anomaly detection network
	train patchcore anomaly detection network
	optimal anomaly threshold for set of anomaly scores and corresponding labels

detect anomalies using deep learning

	detect anomalies using fully convolutional data description (fcdd) network for anomaly detection
	detect anomalies using fastflow network
	detect anomalies using patchcore network
	classify image as normal or anomalous
	predict unnormalized anomaly scores

visualize and evaluate results

	predict per-pixel anomaly score map
	overlay heatmap on image using per-pixel anomaly scores
	view anomaly detection results
	evaluate anomaly detection results against ground truth
	anomaly detection metrics

topics

anomaly detection using deep learning is an increasingly popular approach to automating visual inspection tasks.

featured examples

detect anomalies in pills during live image acquisition

detect anomalies in pills during live image acquisition.