Cognex simplifies neural network training with VisionPro Deep Learning
With VisionPro Deep Learning, Cognex offers a deep learning-based image analysis software specifically designed for factory automation.
For tasks in industrial image processing that can be solved by formulating rules, traditional machine vision systems are usually the right choice. However, these systems often reach their limits when the objects to be inspected introduce variability and cannot be easily automated using rules-based programming. This problem occurs in the inspection of food, in the evaluation of soldered or welded seams, and in many other applications. For use cases like these, machine vision systems based on deep learning technologies have established themselves as an innovative solution in recent years.
Until now, the application of deep learning methods required extensive knowledge of the technology. VisionPro Deep Learning from Cognex significantly reduces this entry barrier through a graphical user interface that simplifies the training of the neural network for users.
VisionPro Deep Learning tools are trained with images, unlike traditional image processing methods which are programmed with rules-based algorithms. VisionPro Deep Learning's intuitive graphical user interface provides a simple environment for controlling and developing applications and significantly reduces the effort required to collect images, train the neural network, and test it on different sets of images.
Users can choose from four image analysis tools in VisionPro Deep Learning. They are optimized for vision inspections in factory automation applications and therefore only require a small number of images for rapid training. Blue Locate, Red Analyse, Green Classify and Blue Read tools solve applications that are too complex for traditional rule-based image processing approaches.
According to Cognex, one major benefit of VisionPro Deep Learning is the unique ability to combine the available tools. This allows complex problems to be broken down into smaller individual steps to simplify project optimisation and reduce the number of training images required. Another special feature of the new software environment is that developers can easily select the best tool for the given task by combining it with VisionPro's rule-based vision libraries. Projects can be created in the VisionPro Deep Learning development environment and then exported to a VisionPro project. In this way, VisionPro and VisionPro Deep Learning offer the possibility to combine the best of both worlds.
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