AI-based image processing for industrial quality control

Rugged image processing solutions for complex and diverse production environments

Automate quality control and visual inspection –
Reduce waste, increase efficiency, and streamline your processes.

  • Detect errors automatically
  • Monitoring processes in real time using deep learning
  • Optimize existing systems
  • Automate industrial inspection
  • Seamless integration into the existing IT infrastructure
Erkennung von 3 Muttern auf einem rostigen Hintergrund
Detection of 3 nuts on a rusty background

I specialize in computer vision for industrial applications and can assist you every step of the way, from feasibility studies to production integration!

Have your use case reviewed!

Typical Use Cases

Qualitycontrol

Detection of OK/Not OK states in production lines (classification)

Error detection

Identification of defects, scratches, or irregularities (object recognition)

Process monitoring

Continuous visual analysis for process optimization

Solutions

Visual inspection systems

Anomaly detection

Data Pipeline & Integration

AI-powered systems for the automated evaluation of components

Detection of unknown errors without extensive training data

Processing, storage, and integration into existing systems

Results & Benefits

98%

Potential recognition accuracy depending on the application

Realtime

Hardware-dependent real-time monitoring

-30%

Typical reduction in scrap rates thanks to automated inspection

Technical Integration

Plattforms

  • Windows (Standalone or Production-PCs)
  • Linux (Server, Edge Devices)
  • macOS

Frameworks

  • PyTorch / TensorFlow for AI Image Processing (Deep Learning)
  • scikit-learn for machine learning models
  • ONNX for cross-platform inference
  • OpenCV for classic algorithms

Process

Analysis

Evaluation of the use case and the data available

Proof of Concept

Development and validation of an initial model

Optimization

Continuous improvement until defined test cases are successfully completed

Integration

Integration into existing systems and processes

Let’s review your use case

Schedule a consultation now

Frequently Asked Questions

AI-based quality control uses computer vision and machine learning to automatically detect defects and monitor production processes.

When the data is highly variable or traditional algorithms fail to detect all cases.

The amount of data required always depends on the use case. With techniques such as data augmentation or transfer learning, even a very small dataset is sufficient.

Yes, integration into existing systems is often possible. Alternatively, a standalone system can be developed.

Typically, a proof of concept takes 2–3 weeks, but the turnaround time may vary depending on the application.