Neural Networks - the Basis for Deep Learning
Artificial intelligence based on neural networks is proving to be an extremely powerful methodology in machine vision. For this reason, the approach has now established itself as a key analytics tool.
While conventional image processing is based on rules and a defined model of the test object for pattern recognition, Deep Learning / Machine Learning (DL/ML) is based on a self-learning methodology.
The reason for such impressive capabilities is the self-learning aspect. In this approach, a neural network is presented with image data and the desired decisions. Using a large amount of this data, the system is able to determine an appropriate solution path. The patterns and properties leading to the decision are derived independently.
It is assumed that the decision path can be considered universal with increasing statistical variance of the input data. This makes it possible to apply the method accurately to the analysis of previously unknown data.
Analyze image data with neural networks - intuitively and flexibly
The implementation of Deep Learning in the industrial environment places high demands on companies that want to use this technology.
Vision AI is a complex research topic in computer vision with a variety of approaches, both directly algorithmic and in terms of a structured development process for customized applications.
Neuralyze® makes it easier to get started with this technology by implementing tried-and-tested solutions as part of an integrated software platform. The tool provides all the steps for successful implementation in an intuitive user interface. This makes it possible to use AI in optical quality assurance even without expert knowledge.
But experts also benefit because Neuralyze® simplifies recurring tasks. It offers standard functionality that creates the freedom to focus on the essentials of a problem and therefore accelerates the operationalization of solutions.
Get to your goal faster with Neuralyze®.
What makes Neuralyze different from other AI tools?
Since 2019, senswork has been developing Neuralyze as an easy-to-use and scalable software concept for deep learning applications. The functionality is essentially based on the requirements and practical experiences that have been and are being demonstrated in the course of senswork's implementation of a large number of vision AI systems.
Neuralyze offers proven Deep Learning approaches such as classification, object detection or semantic segmentation. Furthermore, it is able to read fonts reliably. By concatenating several neural networks, hybrid approaches can also be realized. This makes many more complex problems achievable.
As a platform, Neuralyze® represents the concept of Machine Learning Operations (MLOps). It thereby combines all necessary steps from the management of image data to the customized, executable solution. In combination with senswork VisionCommander®, immediate deployment in industrial high-throughput environments is consistently possible.
In the development of Neuralyze®, senswork places great emphasis on software ergonomics and low-threshold accessibility to pave the way for experts and newcomers alike to easily implement AI quality assurance systems.
Automotive
Industry
Deep Learning reliably inspects complex shapes and highly individual parts with uncooperative surfaces. Neuralyze helps minimize rejection rates and ensure the highest quality.
Automotive
Industry
Food Industry
Industry
Multi-layered features or large differences in the expression of features are common in grown products or even in dough and baked goods. Deep Learning is particularly powerful in this regard.
Food Industry
Industry
Medical Technology
Industry
The production of medical devices is subject to the highest standards, requiring exceptional performance in quality assurance. Deep Learning enables 100% inspection.
Medical Technology
Industry
Electronics Manufacturing
Industry
Component diversity, miniaturization and high integration density pose a major challenge. Deep Learning improves the possibilities for automated inspection of electronic parts.
Electronics Manufacturing
Industry
Polymers Technology
Industry
For the high-quality evaluation of injection molded parts, foils or laminates, Deep Learning provides the necessary flexibility to clearly distinguish acceptable or material-related deviations from defect patterns.
Polymers Technology
Industry
Agriculture
Industry
Differences between plants of the same variety or general changes associated with plant growth - AI offers the flexibility and robustness required for such constraints.
Agriculture
Industry
Classification
Classification is the process of assigning a complete image data set to one or more property classes. Very often, this is used to perform a binary classification that distinguishes between faulty and perfect objects. In addition, the method allows the definition and assignment of multiple classes. A statement is always made globally for the entire input image.
Object Detection
Often, the location of objects or features in an image is relevant. The object detection method is able to determine instances of objects as well as their position within an image. This makes counting objects very easy. The position information is determined by an enclosing rectangle, the so-called bounding box.
Semantic Segmentation
The segmentation approach allows to determine the pixel-based extent of objects belonging to a certain class. As a result, bounding polygons are generated that return accurate information about the perimeter of all surface objects belonging to a class.
Character Recognition (OCR)
Optical Character Recognition (OCR) is a methodology that has been in use for many years in industrial image processing. The implementation with the help of Deep Learning extends the flexibility of the character recognition enormously, so that the reliability of readings increases strongly with simultaneously reduced effort in the learning phase.
3D Deep Learning
Neuralyze is able to evaluate real 3D point clouds using Deep Learning. This makes it possible to evaluate the complete information contained in the 3D data - in contrast to the typically applied method, which uses so-called depth maps instead of 3D data, which are rasterized 2D projections of 3D datasets.
Model Chaining
Model chaining is the sequential linking of different methods or models in a sequence. This can be used to create complex evaluation workflows. A popular hybrid approach is object segmentation, which combines object detection and semantic segmentation. In principle, however, any kind of combination is possible in Neuralyze.
Product Guide
Find out how AI-based machine vision ensures the sorting of pasta products in the food industry.
Quality assurance of dowels with AI: Discover the benefits of object detection in assembly technology.
Efficient automation of laser welding of hairpins for electric motors with AI. 100 % inline inspection for optimal quality.
Guide
