AI-based anomaly detection in the field of industrial vision offers numerous advantages, as it is specifically tailored to the requirements of industrial automation and quality assurance. Deep learning algorithms, in particular, are able to recognise differences and anomalies in image data that are difficult for the human eye or traditional rule-based systems to identify.
Advantages
- Simple configuration thanks to pure good data
- Reduced false alarms through optimisation with already collected data
- New unknown anomalies are recognised automatically
- Reduction of production waste due to fast detection of production errors
- Cost savings through continuous improvement of production processes
Defect detection in continuous material
When inspecting the surfaces of continuous webs such as paper, film, rubber, textiles, nonwovens and metals, the spectrum ranges from simple homogeneous materials to complex structured textiles and nonwovens. Different materials are also frequently combined.
Fault detection for individual objects
A component inspection is carried out by an inspection system to which continuously guided objects, e.g. on a conveyor belt, are fed. Such systems are used in production plants in various industries such as automotive, food and beverages, pharmaceuticals and electronics, for example in sorting, packaging and logistics processes.
iam smart camera – The ideal AI-based solution for your anomaly detection
Thanks to integrated algorithms and real-time processing, the iam recognises complex patterns and adapts to new conditions without external computing power. Anomalies are detected immediately and the production process is optimised. Its scalability enables easy integration into existing systems.
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