Dealing with corrosion (e.g. rust) on offshore structures such as offshore wind turbines is both expensive and difficult. Much of the work takes place manually in hard-to-reach areas, and it requires both time, money and high security. In addition, there are quite a few regulations and industry standards in the field that must be adhered to, and those things combined make the current process time-consuming, unstructured and not efficient.


The PACMAN project develops and demonstrates the application of predictive corrosion management – a method that digitizes corrosion monitoring. With the help of machine learning, the program can detect corrosion, assess the extent of the damage and the need for treatment and, not least, predict the need for maintenance in the future.

The tools to be able to perform these automatically predictable corrosion determinations are: automatic position marking, improved corrosion detection through more advanced camera technology, improved machine learning methods for visual detection of corrosion in 2D images, and automatic transfer of 2D image corrosion results to a 3D interpretation for more efficient handling of these corrosion results.

The predictive corrosion identification tool must be able to provide a draft work order and most importantly: drastically reduce time on corrosion management costs.



Offshore transport reduced by up to 67%

Printed documents reduced by 100%

Time spent on the entire process reduced by up to 85%


Problem owners


Problem solvers

Semco Maritime 


MM Survey 

Aalborg Universitet 

Press clippings about the project: 


“We can potentially eliminate two-thirds of the cost of spotting corrosion, and we can correspondingly reduce the time to analyze whether something needs to be done about it,” says Alexia Jacobsen, Head of Innovation at Semco Maritime: “Our customers would buy it tomorrow if they could.”

“Today, it is very time-consuming to observe and assess the extent of corrosion. We want to develop an innovative method using machine learning to both detect corrosion and predict the need for maintenance,” says Jeppe Lützhøft, Senior Electrical Engineer, who came up with the idea for digitizing corrosion management.

Previous sub-project:

Automatic Corrosion Management 

Project timeline

PHASE 1: Conceptualization
PHASE 2: Development and Testing
PHASE 3: Demonstration and Validation
PHASE 4: Commercialization

Financed by


Start: January 2022
End: December 2024

Total budget: DKK 15.84 million
EUDP funding: DKK 9.78 million

Contact person

Thomas Vohs-Ahlers

Thomas Vohs-Ahlers
Head of Members & Sales
Tlf: +45 5389 2050