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CONCRETE CRACK DETECTION

We develop concrete crack detection systems using proven computer vision algorithms and artificial intelligence. Our crack detection system is designed for visual inspection of precast concrete elements. The use of standard industrial cameras enables cost-effective deployment.

AUTOMATED VISUAL INSPECTION

Automated visual inspection systems are an element of a successful Industry 4.0 strategy of many companies. We design, realize and implement customized software solutions for your use case. In doing so, we use state-of-the-art image processing algorithms and methods to maximize the success rate of your system.

APPLICATIONS

QUALITY CONTROL FOR CONCRETE PRODUCTS

Our customer provides quality control systems for manufacturers of concrete products and uses our software to obtain defect information such as cracks and spalling from 3D image data.

FULL CONTROL SYSTEM FOR RAILWAY SLEEPERS

This use case benefits from a combination of line scan cameras and 3D cameras to meet the high quality requirements in the production of railroad sleepers.

AUTOMATION IN PRECAST CONCRETE PRODUCTION

This computer vision system uses image data of fresh concrete for quality control of the production steps in a fully automated precast concrete production in Switzerland.

MACHINE LEARNING UNDERGROUND

The market leader relies on our software in its premium tunnel boring machines to perform the final quality inspection of tunnel segments underground.

MEET US.

We participate in the following events:

BetonTage 2022, Ulm

22. February 2022 - 24. February 2022

Find us at our booth in the start-up area at Europe's largest precast congress!

BAUMA 2022, Munich

24. October 2022 - 30. October 2022

Meet us at the World's Leading Trade Fair for Construction & Mining Machinery!

OUR TEAM.

LUCAS STEINMANN
LUCAS STEINMANNCo-Founder & Technical Lead
JONAS FEHRENBACH
JONAS FEHRENBACHCo-Founder & Operational Lead
DAVID FEHRENBACH
DAVID FEHRENBACHCo-Founder & Business Lead
PATRICK LAUER
PATRICK LAUERBusiness Development
SAAD
SAADSoftware Development
LUKAS
LUKASResearch & Labelling

JOIN US.

We are a young and motivated team based in Lahr, constantly looking for and implementing new ideas and innovations. Would you like to join the team? Take a look at our open positions and apply

You are a hacker in your studies and would like to apply your skills in a small team? Then you are right with us!

Using various recently developed anomaly detection methods, visible damages should be detected as anomalous regions in images.

Example literature:

  • Improving Unsupervised Defect Segmentation by Applying Structural Similarity To Autoencoders (Bergmann et al. 2019)

A popular approach to concrete damage detection is semantic segmentation by convolutional neural networks. Existing state-of-the-art techniques should be compared and advanced.

Example literature:

  • Machine Learning for Crack Detection: Review and Model Performance Comparison (Hsieh et al. 2020)

LATEST
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OUR CUSTOMERS AND PARTNERS:

FZI
VMT
KIT
KIT

ANY QUESTIONS?

GET IN TOUCH TODAY.

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