Automated Visual Inspection
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 Automated Visual Inspection Library for Computer Vision Toolbox™ 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™.
Functions
Topics
- 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.
Featured Examples
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.
Detect Small Objects Using Tiled Training of YOLOX Network
Detect small objects in full-resolution images using tiled training of a you only look once version X (YOLOX) deep learning network.
Detect Defects Using Tiled Training of EfficientAD Anomaly Detector
Detect and localize defects on anomalous chewing gum images by training an EfficientAD anomaly detection network on tiled normal images.
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.
Identify Defects in Air Compressors Using Spectrogram Images
Detect and localize defects in acoustic recordings of air compressors using Mel spectrogram images and an EfficientAD anomaly detector.
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.
Classify Defects on Wafer Maps Using Deep Learning
Classify manufacturing defects on wafer maps using a simple convolutional neural network (CNN).
Detect Anomalies in Pills During Live Image Acquisition
Detect anomalies in pills during live image acquisition.
Detect Image Anomalies Using Explainable FCDD Network
Use an anomaly detector to distinguish between normal pills and pills with anomalous chips or contamination.
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.
