Automated Visual Inspection
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.
Load Training Data
Train Anomaly Detector
|Train fully convolutional data description (FCDD) anomaly detection network|
|Detect anomalies using fully convolutional data description (FCDD) network for anomaly detection|
|Optimal anomaly threshold for set of anomaly scores and corresponding labels|
Detect Anomalies Using Deep Learning
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|
- Getting Started with Anomaly Detection Using Deep Learning
Anomaly detection using deep learning is an increasingly popular approach to automating visual inspection tasks.