Deep learning-based software for industrial image analysis
A breakthrough in complex inspection, part location, classification, and OCR
VisionPro ViDi is the first deep learning-based image analysis software designed specifically for factory automation. Combining artificial intelligence (AI) with VisionPro and Cognex Designer software, VisionPro ViDi solves complex applications that are too difficult, tedious, or expensive for traditional machine vision systems.
This novel approach, which tolerates deviation and unpredictable defects, beats even the best quality inspectors and is ideal for:
- Defect detection
- Texture and material classification
- Assembly verification and deformed part location
- Character reading, including distorted print
Human-like, powerful, and fast
VisionPro ViDi combines the specificity and flexibility of human visual inspection with the reliability, repeatability, and power of a computerized system—all in an easy-to-deploy interface.
|Compared to Human Visual Inspection||Compared to Traditional Machine Vision||Compared to Deep Learning Open Source Libraries|
Operates 24x7 and maintains the same level of quality on every line, every shift and every factory.
Designed for hard-to-solve applications
Solves complex inspection, classification and location applications impossible or difficult with classic rules-based algorithms.
Requires less data and computing power
Training requires hundreds of images instead of millions. Since images are stored locally and require minimal computing resources deployment is fast and affordable.
Identifies every defect outside of the set tolerance.
Easier to configure
Applications can be set up quickly, speeding up proof of concept and development.
Simple training interface
Software is designed for real-world factory conditions, with no special expertise required.
Identifies defects in milliseconds, supporting high-speed applications and improving throughput.
Handles defect variations for applications that require an appreciation of acceptable deviations from the control.
Cognex’s network of engineers and technical experts provide world-class application support.
4 powerful toolsets
Cognex ViDi’s deep learning-based algorithms are optimized for real-world industrial image analysis, requiring vastly smaller image sets and shorter training and validation periods. Choose between Red-Analyze, Green-Classify, Blue-Locate, and Blue-Read tools to solve your highly supervised to completely unsupervised applications.
ViDi Blue-Locate finds complex features and objects by learning from annotated images. Self-learning algorithms locate parts, count translucent glass medical vials on a tray, and perform quality control checks on kits and packages.
Detects anomalies and aesthetic defects
ViDi Red-Analyze segments defects or other regions of interest simply by learning the varying appearance of the targeted zone. ViDi Red-Analyze identifies scratches on complex surfaces, incomplete or improper assemblies, and even weaving problems on textiles simply by learning the normal appearance of an object including its significant but tolerable variations.
Classifies objects or scenes
ViDi Green-Classify separates different classes based on a collection of labelled images. By training on acceptable tolerances, ViDi Green-Classify identifies products based on their packaging, classifies the quality of welding seams and separates acceptable or inacceptable anomalies.
Reads text and characters
ViDi Blue-Read deciphers badly deformed, skewed, and poorly etched codes using optical character recognition (OCR). The pretrained font library identifies most text without additional programming or font training for fast, easy implementation. This robust tool can be retrained to adjust to specific OCR application requirements—no vision expertise required.
Easy training and deployment
Cognex ViDi allows technicians to train a deep learning-based model in minutes, based only on a small sample image set. Once the application is configured, ViDi delivers fast, accurate results and saves images for process control.