Artificial Intelligence at the Edge.
Moving data processing closer to the data source, i.e. edge computing, has many advantages and is often necessary for various reasons. For example, when larger amounts of data are involved (e.g. image data) or external cloud services cannot/should not be used for reasons of data security.
Why "AI at the Edge"?
"AI at the Edge" offers you the following advantages:
- Real-time processing: Immediate on-site data analysis and decision-making for faster response times.
- Data protection and security: Local processing reduces the transmission of sensitive data via networks, strengthens data protection and minimises security risks.
- Increased efficiency: Targeted transmission of relevant data reduces data traffic, resulting in more efficient use of resources.
- Robustness and resilience: Local processing ensures system robustness and resilience, independent of centralised cloud servers.
- Adaptability: Local data interpretation enables adaptive and context-sensitive adjustment to changing conditions and requirements.
However, just like the edge system itself, the software and algorithms themselves must be highly integrated and efficient in order to be able to make the right decisions at the right time.
Our Methods.
Our team specialises in precisely this optimisation and the integration of data processing algorithms and artificial intelligence (AI) for and in edge systems and supports you in the following areas:
- Model optimization: Reduction of model size and complexity for resource-efficient execution on edge devices.
- Federated learning: Local training of AI models without transferring sensitive data to central servers.
- Security protocols: Implementation of robust security mechanisms to protect data integrity, confidentiality and authentication at the edge.
- Edge management platforms: Efficient platforms for monitoring, updating and managing AI models in distributed edge environments.
- Edge development frameworks: Special frameworks to simplify the development, optimisation and integration of AI applications at the edge.
Areas of Application & Success Stories.
The areas of application for edge AI are diverse. We work with image data as well as classic sensor data ... Below you will find details of applications that we have already successfully implemented for our customers:
Industrial cameras can be used to detect defects or changes in the surface, shape, or color of an object with the help of AI. This can significantly increase the quality and efficiency of processes, especially during visual inspection within or at the end of production lines or machines. We have developed our own algorithms for this purpose, which are trained with just a few (10-20) images and still achieve a high recognition rate of over 98%. Our systems can also work with reflective and transparent surfaces.
Applications: Production and processing machines for baked goods, metal objects, glass, plastic, wood, processing of electronic products and PCB assembly
AI models can detect hands, feet, eyes, faces and entire people very reliably. The position of individual body parts can also be determined.
Even if only parts of them are visible in the image, these models already work very reliably. The models can also directly blur people's faces, making them GDPR-compliant.
This allows completely new applications to be realized:
Ensuring workplace safety, supporting assembly processes, dynamic path planning for mobile robots, human-machine interaction, and much more.
Wherever information is not available in machine-readable form, automating processes is often very difficult. One example of this is printed order information for monitoring goods in logistics. AI models for OCR (Optical Character Recognition) can help here, so that not all processes have to be digitized, but analog information can be digitized very easily. These models are already very powerful and, combined with the ideal hardware, can recognize and interpret texts in real-time.
Applications: Goods management in intralogistics, digitalization of production orders
AI models are trained to precisely map and understand the ideal conditions of a process. The AI model uses various sensors to monitor the process in real-time and adjusts the necessary parameters of the machine or controller if necessary so that the process is always operated with maximum efficiency. In addition, potential errors can be recognized at an early stage or often even avoided altogether.
Applications: Recycling machine, stone crushing machine, bending processes of metallic objects, extrusion processes, injection moulding processes, etc
In robotics, AI can be used in combination with a 2D or 3D camera to make processes much more flexible. To achieve this, the AI first localizes the object in the image that is to be gripped and then calculates the exact position (+/- 1 mm). This information is then used to calculate the path for the robot. Using this method, objects can be found and gripped or moved around without having to place them in a defined position in advance each time or without the robot having to remain in an emergency stop.
Applications: Collaborative robots, industrial robots, mobile robots
To avoid high costs for maintenance or poorly produced parts, it is important to recognize potential errors within the production processes very quickly. AI models can be used at various points to analyze image and sensor data in real-time and immediately trigger an alarm if deviations from the ideal state are detected. Thanks to state-of-the-art algorithms, these models can adapt to changes in a very short time.
Applications: Injection molding machines, plastics production, wood processing, glass production, etc
Tech Stack.
Artificial Intelligence
Hardware Vendors
