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Synthetic Image Data

How AI supports AI model creation
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AI-driven inspection is rapidly transforming industrial quality control. To make these inspection processes even more efficient and reliable, artificial intelligence needs access to a large amount of high-quality, ideally pre-labeled, data for training. In practice, collecting such data is time-consuming and costly, so the required data is often not available in sufficient quantity. Synthetic image data addresses this challenge by providing a scalable way to generate training data, enabling faster and more efficient AI model development while also improving model performance.

ZEISS CT inspection showing internal porosity defects highlighted inside a translucent component.

The challenge: Data scarcity and rare defects

When production companies begin implementing AI for quality control, they often encounter one major obstacle: limited overall data availability in the beginning of the project and for rare but critical defects during the ramp-up. Digitizing and collecting sufficient real-world measurement data is often time-consuming and expensive. Severe defects, though rare, are underrepresented in datasets, making it difficult to train AI models to detect them reliably. This can be very critical in battery inspection for example. These challenges slow down the adoption of AI and limit its potential to optimize production processes.

ZEISS CT scan of a battery cell with dimensional measurement overlay in zeiss inspect x-ray software.

The Solution: What is synthetic image data?

Synthetic image data is image data that is generated artificially and imitates real-world conditions. Using advanced algorithms, simulations, and AI methods, these datasets can be created specifically to overcome challenges such as limited data and very rare defects.

This approach accelerates AI model development by providing large and diverse datasets, including examples of rare defects. Combining synthetic and real data improves model accuracy, because it makes them more robust and reliable.

Additionally, and this might be the biggest advantage, synthetic data comes with labels already included. This removes the need for manual annotation and significantly reduces the time and resources required. ‘Pre-labeled’ means that information like labels, tags, or metadata is automatically added to the raw data in the data generation process process, which is essential for the use in AI applications.

Application for many industries and components

The use of synthetic data enables faster and more reliable anomaly detection across industries and clearly improves the performance of AI models. Because synthetic data is already prepared and labeled, it reduces the time needed for data preparation and annotation. This can speed up the ramp-up of AI inspection processes by up to 25% and minimizes the need for iterative adjustments.

In the battery inspection example, synthetic data helps to build robust AI models that can detect rare but critical defects early and reliably. Synthetic data is also valuable in areas where real-world data is scarce, such as the inspection of casting parts. By integrating synthetic data, companies can automate inspection processes, improve model accuracy and reduce costs while ensuring consistent quality. Whether it is used to train new AI models or to boost the performance of existing ones, synthetic data is a powerful tool for achieving reliable and efficient defect detection.

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Experience AI in quality control

Discover our comprehensive solutions for efficient AI-based inspection processes. Ensure productivity and robust performance across industries and diverse applications. Seamlessly integrated into your workflows, we offer flexible options tailored to your needs.

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We support you in the creation of synthetic data

You don’t have enough data for your components to make effective use of AI-driven inspection? We can help you by generating synthetic data tailored to your application. Contact us and we will discuss the process with you. Of course, synthetic data is fully compatible with our growing portfolio of AI-driven quality control solutions.

Synthetic image data is boosting  AI-driven quality control by addressing data scarcity and enabling faster, more accurate model development. As this technology continues to evolve, it will play an increasingly vital role in helping companies achieve higher production quality and efficiency. At ZEISS, we are committed to driving innovation in this space, empowering our customers to unlock the full potential of AI in quality control. With synthetic image data, the future of industrial quality inspection is here - and it’s smarter, faster, and more reliable than ever before.

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Discover how AI is revolutionizing inspection

As industries and production processes continue to evolve, the role of artificial intelligence in inspection processes is becoming increasingly important. Curious about the current possibilities and how AI can enhance your inspection process? We spoke with Christian Wojek, Head of AI at ZEISS Industrial Quality Solutions, about how AI is shaping the future of quality control.

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