Industrial Image Processing The Perfect Product With Machine Vision

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MVTec Software GmbH

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Automated inspection of 50 components in less than a second? Not wishful thinking, but reality—thanks to machine vision technologies. They offer numerous functions and methods that make quality assurance faster, more efficient, more reliable and more cost-effective.

Comprehensive defect inspection is essential in every manufacturing industry. This is the only way to achieve high quality standards and reduce the production of rejects to a minimum. This increases customer satisfaction and trust, boosts the company's success and ultimately helps to reduce costs. The alternative: inspection processes are carried out manually in production. However, this has clear disadvantages compared to automated solutions, as inspection by employees is significantly slower and more error-prone, not to mention the fact that companies miss out on the benefits of digitalization. In addition, the monotonous work leads to rapid fatigue and reduced attention among the inspectors, meaning that a consistently high quality of the workflow cannot be guaranteed.

Automate Testing Processes

In many cases, it is therefore worth automating testing processes. This paves the way for maximum precision and speed. It also frees up human workers so that they can devote themselves to more demanding tasks. Industrial image processing (machine vision) has proven to be a true all-round technology for automated quality assurance. This is because it provides valuable support to all conceivable manufacturing industries.

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Inspection of A Component Within 20 Milliseconds

Powerful machine vision software can identify objects and product defects on recorded images that remain hidden to the human eye. The process thus takes the entire quality assurance process chain to a new level. Machine vision offers a wide range of technologies for this purpose. These significantly accelerate workflows and enable inspection times of less than 20 milliseconds per component.

With so-called blob analysis, for example, the algorithms extract relevant features from certain image areas (blobs) based on programmed rules in order to analyze them further. For example, an excessive number of edges on an electronic component can indicate a defect. Another rule-based approach is matching. It allows the high-precision determination of the position of objects that have a small variance in their shape. Matching technologies are mainly used for position detection and completeness checks. This involves checking whether all the required components are present and in the correct position. For example, it can be used to determine whether a package contains the correct number of products.

In another inspection application, print image inspection, specialized algorithms are used to compare an image of the print to be inspected pixel by pixel with a reference image (golden sample) and detect deviations. In this way, printed images of banknotes, magazines, etc. can be reliably checked. 3D image processing technologies are also conceivable: they are used for the three-dimensional capture of objects in order to obtain information about their shape and volume. This is helpful, for example, when measuring contents, analyzing fill levels or detecting deformations and defects in pipes.

Image Processing

Industrial Image Processing—Easy and Profitable to Use

Deep Learning Detects Errors With High Variance

In addition to rule-based processes, deep learning technologies are also used to optimize defect inspection: They are primarily used when anomalies exhibit a high degree of variance—as is often the case with natural products. In the food industry, for example, defects on nuts or the appearance of pretzels can vary greatly. Here, it would be very time-consuming to achieve robust results using rule-based methods. Deep learning, on the other hand, learns independently based on comprehensive training with good or bad images and can therefore react more flexibly to deviations.

Another method for optimizing quality assurance is hyperspectral imaging: this is an advanced imaging technology that provides data on material properties in addition to colour information. This is particularly relevant for the food industry: for example, white tablets of the same shape can be distinguished from one another or rotten spots on an apple can be detected that are not visible to the human eye or a conventional camera. Last but not least, a special imaging process for reflective surfaces, known as deflectometry, is used for defect inspection: a projector projects a stripe pattern onto the object while a camera analyzes the reflections and finds defects. This makes it possible to reliably detect dents and scratches on car bodies or cell phone displays, for example.

Analyze 10,000 Images Per Minute in Real Time

All of these technologies are an integral part of MVTec's standard machine vision software products Halcon and Merlic and prove their benefits every day in a wide variety of applications: The company Averna, for example, uses Halcon's image processing technologies such as blob analysis to provide an innovative test and quality solution for a battery manufacturer. In this application, almost 10,000 images per minute are analyzed in real time. As a result, the system reliably detects a wide range of defects and the customer can now deliver impeccable quality.

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Date: 08.12.2025

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Panasonic Energy, a global producer of high-quality car batteries, also benefits from MVTec Halcon: the software supports the entire battery cell assembly process, including visual inspection and packaging. This prevents defective products from leaving the factory. In addition, the use of the machine vision software has reduced the number of batteries mistakenly classified as faulty by 57.3%.

Indispensable for Modern Quality Assurance

With its extensive range of applications and methods, machine vision shows that it is indispensable for modern quality assurance in every manufacturing industry. Its accuracy and speed help to save costs and resources and keep customer satisfaction high through reliable product quality.

About the author

Jan Gärtner has been working at MVTec Software GmbH since 2020, initially as an Application Engineer. He has been Product Manager Halcon since the beginning of 2023.

He studied electrical engineering and information technology at the Technical University of Munich.