Automated Visual Inspection

Automate quality control tasks using anomaly detection and localization methods

Automated visual inspection (AVI) is a set of techniques used to detect and classify defects in manufactured products. Modern visual inspection uses machine learning and deep learning algorithms to assist and improve quality assurance processes.

Custom defect detection must accurately determine the location of defects in a given image and classify the defect category. Generally, you can detect defects of different sizes using state-of-the-art supervised deep learning models such as the latest you only look once (YOLO) algorithms. Best performing models characterize and locate defects in real-time.

You can use anomaly detection deep learning methods to determine whether an image of a manufactured product is normal or anomalous. Additionally, you can produce precise and interpretable results using anomaly localization. This method enables you to create a visualization of the defects using an anomaly map.

The specific detection model 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. For more information, see Getting Started with Anomaly Detection Using Deep Learning.

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™.

Funzioni

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Load Training Data

`groundTruth`	Ground truth label data
`imageDatastore`	Datastore for image data
`sceneLabelTrainingData`	Create training data for scene classification from ground truth (Da R2022b)
`splitAnomalyData`	Split data into training, validation and testing sets for anomaly detection (Da R2023a)

Train Anomaly Detector

`trainFCDDAnomalyDetector`	Train fully convolutional data description (FCDD) anomaly detection network (Da R2022b)
`trainFastFlowAnomalyDetector`	Train FastFlow anomaly detection network (Da R2023a)
`trainPatchCoreAnomalyDetector`	Train PatchCore anomaly detection network (Da R2023a)
`anomalyThreshold`	Optimal anomaly threshold for set of anomaly scores and corresponding labels (Da R2022b)

Detect Anomalies Using Deep Learning

`fcddAnomalyDetector`	Detect anomalies using fully convolutional data description (FCDD) network for anomaly detection (Da R2022b)
`fastFlowAnomalyDetector`	Detect anomalies using FastFlow network (Da R2023a)
`patchCoreAnomalyDetector`	Detect anomalies using PatchCore network (Da R2023a)
`classify`	Classify image as normal or anomalous (Da R2022b)
`predict`	Predict unnormalized anomaly scores (Da R2022b)

Detect and Classify Objects

`yoloxObjectDetector`	Detect objects using YOLOX object detector (Da R2023b)
`trainYOLOXObjectDetector`	Train YOLOX object detector (Da R2023b)
`detect`	Detect objects using YOLOX object detector (Da R2023b)

Visualize and Evaluate Results

`anomalyMap`	Predict per-pixel anomaly score map (Da R2022b)
`anomalyMapOverlay`	Overlay heatmap on image using per-pixel anomaly scores (Da R2022b)
`viewAnomalyDetectionResults`	View anomaly detection results (Da R2022b)
`evaluateAnomalyDetection`	Evaluate anomaly detection results against ground truth (Da R2022b)
`anomalyDetectionMetrics`	Anomaly detection metrics (Da R2022b)

Load Detector for Code Generation

vision.loadYOLOXObjectDetector Load YOLOX object detector model for code generation (Da R2023b)

Argomenti

Getting Started with Anomaly Detection Using Deep Learning
Anomaly detection using deep learning is an increasingly popular approach to automating visual inspection tasks.
Getting Started with YOLOX for Object Detection
Use the YOLOX object detector to detect small objects with improved performance.

Esempi in primo piano

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.

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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.

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Classify Defects on Wafer Maps Using Deep Learning

Classify manufacturing defects on wafer maps using a simple convolutional neural network (CNN).

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Detect Image Anomalies Using Explainable FCDD Network

Use an anomaly detector to distinguish between normal pills and pills with anomalous chips or contamination.

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Detect Anomalies in Pills During Live Image Acquisition

Detect anomalies in pills during live image acquisition.

Detect Image Anomalies Using Pretrained ResNet-18 Feature Embeddings

Train a similarity-based anomaly detector using one-class learning of feature embeddings extracted from a pretrained ResNet-18 convolutional neural network.

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