COMPUTER VISION PROJECTS
Home2021-02-09T16:55:46+01:00

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 usage of regular industrial cameras allows low-cost deployment.

INDUSTRIAL IMAGE PROCESSING

Automated visual inspection systems are one element of a successful industry 4.0 strategy of many companies. We design, realize and implement tailor-made software solutions for your use case. Thereby we use state of the art image processing algorithms and methods to maximize the sucess rate of your system.

MEET US

We participate in following events:

OUR TEAM.

High quality products only exist with high performing teams.

LUCAS STEINMANN
LUCAS STEINMANNTechnical Lead
JONAS FEHRENBACH
JONAS FEHRENBACHOperational Lead
DAVID FEHRENBACH
DAVID FEHRENBACHBusiness Lead

Join Us

We are a young and motivated team based in Lahr and constantly searching and realizing new ideas and innovations. You would like to join the team? Check out 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!

You are a sales talent or a research genius in your studies who supports our Business Lead to win more clients and sales partners for us? We are waiting for you!

Your Tasks

  • Serve customers by selling products and meeting customer needs.
  • Adjusts content of sales presentations by studying market and customers.
  • Contributes to team effort by accomplishing related results as needed.
  • Recommends changes in products, service, and policy by evaluating results and competitive developments.

Your Skills:

  • Self-confidence
  • Product knowledge
  • Presentation skills
  • Client relationships
  • Motivation for sales
  • Customer service

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
ARTICLES

Supporting the 4th Social Hackathon Lahr

Like every year we supported the CodeWeekEU. At the 4th Social Hackathon of our neighbors IntegrA Lahr gemeinnützige GmbH our whole team was involved as supporters. Some of the measurement points visualized on the ttnmapper Due to [...]

Our Partners:

FZI
VMT
KIT
KIT

ANY QUESTIONS?

GET IN TOUCH TODAY.

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