Computervision for quality assurance

Computer Vision for Quality Assurance

Artificial Intelligence for production process automation

From challenges to solutions

The Challenge

Detecting quality defects is often crucial to prevent customer dissatisfaction or even damages in a production line. To guarantee a high quality standard there is a constant inspection in place, today often manually.

The Idea

Our approach is to use Computer Vision for these inspections to avoid manual work in routine cases.

The Solution

A combination of classical image processing and latest Machine Learning approaches allows to detect and distinguish known objects from foreign objects or ones with unexpected variations.


Surface imperfections & foreign objects

In production, there are irregularities which can be tolerated and others that have to be detected. Shipping material with surface imperfections will result in customer dissatisfaction. Conveying foreign objects to a machine can easily result in severe damages to the machine.

A lot of manual work

Both cases are costly and require extensive quality assurance activities. Today, inspections are often done manually which is not optimal for several reasons. On the one hand, this is often an unfulfilling, repetitive task which still requires high concentration. On the other hand, it comes with high costs, especially as humans can perform such inspection only at a certain rate.


We apply Computer Vision methods to perform such QA tasks. A common scenario is a camera looking at a certain part of a machine or production line. However, our approach is not limited with regard to the sensor but works on several data sources including infra-red cameras, line-scanners, or x-ray technology.

We read in pixel data and compute a decision if the content meets the acceptance criteria or not.


We look at images showing flawless manufactured items as well as at images of different types of defects. Among the methods we apply are classical approaches such as image enhancement or background removal, but also Machine Learning algorithms for object detection and classification.

We have several tools supporting this process, such as ones for annotating test and training data or ones for managing Machine Learning pipelines including evaluation and hyper parameter optimization. In the end, we optimize the learned models for efficient use in production.

Let’s have a look at two specific uses cases.

Use Case 1:
Surface imperfection detection

Imperfections of a material’s surface can be an aesthetic issue on the one hand but on the other hand prohibit its usage in certain scenarios. To guarantee the right level of quality of a manufactured item, we detect any visible irregularities. In addition, we not only compute metrics such as the shape or the extent of a defect, but also apply classification with regard to its type. While certain types of defects might be tolerable, others will be blockers. Also this information helps to identify the cause of the defect.

Use Case 2:
Foreign object detection

In a production line or during packaging, very detailed assumptions about the incoming material are made. Foreign objects being conveyed into a machine might destroy the machine or, when packaged and delivered harm or at least annoy the consumer. Thus, the detection of any foreign object is necessary in order to take respective actions. In the figure above, you see a stone that was erroneously harvested together with apples and that needs to be separated before any subsequent treatment.

How to start?
  • Get in touch with us to see how Computer Vision methods can improve your quality assurance process!
  • Prepare some image material from your production line. We will develop a small prove-of-concept prototype to show you what today’s algorithms are capable of.

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