Industrial image processing is often seen as complex, expensive, and something only specialists can handle. For a long time, that was indeed the case. Artificial intelligence is fundamentally changing that: Industrial image processing powered by AI reliably detects even the smallest deviations and can now be set up by foremen and supervisors without any programming knowledge. Before we show you how it works, here’s a brief overview:

What is industrial image processing?

Industrial image processing, also known as machine vision, refers to the use of cameras and software to automatically inspect components and products in a manufacturing environment. Put simply, the system captures an image, analyzes it, and makes a decision based on that analysis—pass or fail.

It is used wherever quality matters: from completeness and dimensional inspections to defect and surface inspections, as well as for worker guidance and plant control. Compared to manual visual inspection, it offers three advantages: it operates with consistent accuracy around the clock, documents every result in a traceable manner, and scales effectively as production volumes increase.

Traditional Systems vs. AI: What's Changing

Traditional image processing is rule-based: for each component, a specialist defines fixed parameters—position, shape, color, and threshold values. This works as long as nothing changes. In practice, however, changing lighting conditions, different viewing angles, and new variants constantly cause deviations. The result is either overlooked defects or too many false alarms—so-called pseudo-defects—and every adjustment must be made through IT or external service providers.

AI takes the opposite approach: Instead of rigid rules, a model learns from real production images what a good part looks like and what a defective part looks like. This enables it to reliably identify components even under changing conditions, detect even the smallest defects without increasing the false positive rate, and even automate inspections that previously could only be performed by the human eye. The article From Human to AI illustrates how this is transforming quality inspection as a whole.

The key benefit: The effort shifts from the tedious task of programming individual rules to simply providing images. This is exactly where the no-code approach comes in.

No-Code: Image Processing Without Programming Skills

The key step is to take the creation of AI models out of the hands of programmers. With a no-code approach, the people who know the process best—master craftsmen, foremen, and quality control managers—create the inspection programs themselves.

This has two consequences that make all the difference in day-to-day manufacturing:

• No IT bottlenecks. Adjustments and new variants are implemented directly on the production line, rather than waiting for a service provider.
• Process expertise is incorporated. Those who assemble the product on a daily basis know best what matters during inspection.

This transforms industrial image processing from a niche topic into a tool that can be used by the entire team.

How AI-based image processing works in practice

In the application, the AI-based image processing operates as a closed-loop system:

1. Capturing images: The inspection system automatically captures images from the production line during operation.
2. Create an AI model: Using an AI builder, you can train your own models from these images in just a few clicks—no programming knowledge required.
3. Use the model: The finished model is exported and immediately used offline in the testing system.

Depending on the inspection task, different types of AI are used: object detection(is the correct component present?), classification (polarity, colors, markings, etc.), anomaly detection (learns from good images and flags any deviations), and segmentation (e.g., for cables or protective coatings). With each new batch of images, the models become more accurate.

What benefits does this offer for manufacturing?

The benefits are directly reflected in the key metrics that matter most to production and quality managers:

• Fewer complaints, because errors are identified and corrected as soon as they occur.
• Reduced waste, through early, comprehensive inspection.
• Higher productivity, as manual follow-up checks and rework are reduced.
• Digital traceability: Every inspection is documented—a benefit for legal certainty and process analysis.
• Fast return on investment: In typical use cases, a system pays for itself in less than nine months.