PEKAT VISION offers a powerful set of deep learning modules for industrial inspection — including Anomaly Detection, Surface Detection, Detector, Classifier, and OCR.
These can be combined and extended with scripting to solve complex inspection tasks. For most applications, no programming is needed — PEKAT VISION includes everything required for effective quality inspection.
Deep Learning Modules
Anomaly Detector
The Anomaly Detection module identifies previously unseen defects using unsupervised deep learning. It requires only a small set of defect-free images for training and no manual labeling. This makes it ideal for products with irregular patterns or complex backgrounds.
Surface Detection
The Surface Detection module is designed to detect visible surface defects such as rust, abrasions, cracks, and inclusions — even on heterogeneous or textured materials. It uses supervised deep learning and requires training on images with annotated defects.
Object Detector
The Detector module locates and optionally classifies objects or defects — even those with variable shapes such as fruits, wood knots, or scratches. It requires annotated examples of each object or defect. Object orientation does not affect detection.
Classifier
The Classifier module assigns a single class to an entire image, to objects detected by other modules, or to objects in fixed positions. Each object or image is classified into one predefined category.
Optical Character Recognition (OCR)
The deep learning OCR module reads alphanumeric characters, symbols, or text strings from various surfaces — including stamped part numbers, serial numbers, or batch codes — for tracking and verification.
Editing Tools
Preprocess
Preprocessing lets you rotate, crop, scale, normalize backgrounds, and more — preparing images for accurate and efficient analysis.
Unifier
Unifier ensures consistent object alignment in position and orientation for accurate further processing.
Mask
Masking is an alternative to the crop function when cropping is not possible or when different parts of an image require different inspection settings.
Enhancements
Parallelism
Parallelism enables multiple processing branches to be defined independently. Each branch runs in sequence, and their results are combined afterward. As of version 3.14, no programming is required to configure such multi-branch processing.
Code
The Code module allows you to extend PEKAT VISION with Python. Use it for advanced preprocessing (e.g., with OpenCV or NumPy), custom logic, or integration with external systems and interfaces.
Operator View — GUI Builder
The Operator View feature lets you design a simplified user interface for line operators, showing only selected controls and outputs relevant to their tasks.
Measure Tool
The Measure module allows you to determine the dimensions of physical objects by calculating the distance between two user-defined points.
Other Features
Confusion Matrix
This powerful tool evaluates the performance of trained models by comparing user annotations with model predictions, offering valuable insights for fine-tuning your models.
HTML Report
The Report module automatically creates an HTML report containing statistical data, optionally includes training images, evaluated test images, and a visual representation of the processing flow.
Output
The Output feature allows triggering an action once an image is processed (ALL, OK, or NG). You can execute a script via the command line, send an HTTP request (GET or POST), communicate over TCP/IP, or connect directly to a Siemens PLC.
Get In Touch
Do you need more information? Let us know, we will answer any question you may have.
PEKAT VISION is now part of Datalogic Group
