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Anomaly detection with AI
in industrial image processing

Automated quality control systems are becoming increasingly important in modern manufacturing. One of the technologies used here is the identification of anomalies based on machine learning. These systems make it possible to detect deviations from the normal state of products or components and thus eject defective parts from the production process.

The basic idea behind anomaly detection is simple: the system is trained with "iO" images, i.e. images of correctly manufactured products. The AI learns on the basis of this normal data and can then identify anomalies.

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Strengths of
Anomaly detection through AI

The detection of anomalies offers concrete advantages for very stable and well-defined production processes:
  • Fast commissioning:
    As only iO images are required, the solution can be implemented relatively quickly.
  • Simple detection of deviations:
    The system enables defective products to be identified as "deviating" at an early stage.
  • Pragmatic solution for stable products:
    For products with clear, repeatable characteristics, this method is suitable for carrying out quick quality checks.

Limitations of unsupervised anomaly detection

Despite its many advantages, anomaly detection reaches its limits in many applications:

Reduction to "iO vs. NiO":

The algorithms do not provide a detailed analysis of the type of error or the exact pattern. In-depth root cause analysis or process optimization is therefore hardly possible.

Sensitivity to changing conditions:

If lighting conditions, surface reflections or varying components change, the error rate (pseudo error) increases significantly and the detection accuracy decreases at the same time.

Hardly any added value for process optimization:

As no differentiated information about the type of deviation is provided, the benefit for targeted optimization of processes remains low.

Documentation and traceability severely restricted:

Models are trained with normal sample images using machine learning. Evaluations of the anomalies only provide an indication of the reject rate. Additional manual inspections required if patterns are to be recognized.

These limitations should be taken into account when choosing anomaly detection. The technology is suitable for simply structured, stable production processes, while for complex or varying products other approaches, such as data-centric deep learning methodsare more efficient in the long term.

Machine vision anomaly detection
in Industry

The identification of anomalies using artificial intelligence offers a pragmatic introduction to automated quality control. It enables rapid implementation, reduces the initial outlay for training data and is suitable for stable, standardized production processes. At the same time, the method is limited to simple "iO vs. NiO" decisions and does not provide in-depth analyses for process optimization.

Companies can use this technology to detect product defects and reduce waste in the short term, but should check whether alternative approaches make more sense for long-term, flexible quality solutions. The challenges of delivering error-free products in real time become greater for more complex production processes or products with a high number of variants. The use of data-centric AI solutions is often recommended to enable more precise error analysis and sustainable process improvements.

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Model-centric AI, anomaly detection with AI and machine vision systems can deliver excellent results in clearly defined, stable production environments - especially if error patterns are known and processes are largely constant.

In many modern production facilities, however, components, materials or process parameters change regularly. In such cases, classic systems quickly reach their limits, as they are dependent on fixed models or rules.

If you are looking for a solution that constantly adapts to new data, reliably detects complex error patterns and works stably despite varying conditions, we recommend the data-centric deep learning approach.

Find out here how data-centric AI can take your quality assurance to the next level.

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