News
about GEFRA
OPTISORT inspection system with „deep learning“ for easy defect detection.
In February 2024 GEFRA GmbH startet the newest development for visual inspection.
Deep learning technology excels the standard vision application in defect detection, specifically in instances where there are complex and varying imaging conditions.
The integration of this technology into the OPTISORT testing system offers users completely new perspectives.With deep learning, defective parts and good parts are learned and classified accordingly.These datasets are trained using multi-layer neural network architectures.Based on these templates, the test items are classified as good or defective parts.Deep learning requires a lot of computing power. High-performance GPUs have a parallel architecture that is efficient for deep learning. The easy operation of this application is also ensured by the user-friendly AutoControl testing software.
Deep learning technology, is making machine vision technology for automated visual inspection more accessible and capable.
Key to deep learning is the training of a neural network model. AutoControl interactive environment provides the platform for training these models for use in machine vision applications.
AutoControl delivers all the functionality needed for this task, so you can create and label the training image dataset; augment the image dataset, if necessary, and train, analyze, and test the neural network model.
Artificial intelligence, specifically machine learning by way of Deep learning technology mimics how the human brain processes visual input but performs this task with the speed and robustness of a computerized system. The technology works to ensure quality in manufacturing industries, controlling production costs and enhancing customer satisfaction.
The technology still benefits from conventional image processing and analysis to locate regions of interest within images to speed up the overall process and make it even more robust.
During the FASTENER SHOW in October you will see the inspection system OPTISORT in function with the integrated „deep learning“ system.
Deep learning technology excels the standard vision application in defect detection, specifically in instances where there are complex and varying imaging conditions.
The integration of this technology into the OPTISORT testing system offers users completely new perspectives.With deep learning, defective parts and good parts are learned and classified accordingly.These datasets are trained using multi-layer neural network architectures.Based on these templates, the test items are classified as good or defective parts.Deep learning requires a lot of computing power. High-performance GPUs have a parallel architecture that is efficient for deep learning. The easy operation of this application is also ensured by the user-friendly AutoControl testing software.
Deep learning technology, is making machine vision technology for automated visual inspection more accessible and capable.
Key to deep learning is the training of a neural network model. AutoControl interactive environment provides the platform for training these models for use in machine vision applications.
AutoControl delivers all the functionality needed for this task, so you can create and label the training image dataset; augment the image dataset, if necessary, and train, analyze, and test the neural network model.
Artificial intelligence, specifically machine learning by way of Deep learning technology mimics how the human brain processes visual input but performs this task with the speed and robustness of a computerized system. The technology works to ensure quality in manufacturing industries, controlling production costs and enhancing customer satisfaction.
The technology still benefits from conventional image processing and analysis to locate regions of interest within images to speed up the overall process and make it even more robust.
During the FASTENER SHOW in October you will see the inspection system OPTISORT in function with the integrated „deep learning“ system.
