“We are impressed by the accuracy in eSmart Systems’ Grid Vision® software for rust detection. In a dataset of 7,500 pictures, the algorithm found rust, with 96% accuracy. With digital recording of data and images we are able to detect changes in rust over time, which can be very valuable to us in the future.”

- Lars Rasmussen, Senior Engineer, Energinet

In a nutshell

Grid Vision allowed Energinet to instantly detect and quantify rust on their towers.

The customized solution enables predictive maintenance and utilizes inspection results to extend the lifetime of individual towers. 

The challenge 

High-voltage transmission towers are installed in a wide range of locations and environments and are exposed to all possible weather conditions. Due to this, transmission towers are prone to corrosion and can degrade over time. 

Energinet needed a solution to effectively spot and quantify the type and level of rust on its transmission towers and to be able to detect changes over time.

Our solution

In order to match Energinet’s specific needs, we developed and trained customized AI models. Thirty-six annotated pictures were initially made available by Energinet to train the models. The dataset was further enhanced by augmented data techniques and the use of synthetic images from our simulator. The resulting training dataset consisted of 285,025 examples of rust. 

Once trained and deployed to the product, our customized model was able to detect rust with high accuracy, quantify the amount of rust on the pictures and easily visualize it for Energinet’s experts.


Grid Vision identified rust occurrences in a large dataset of images with 96% accuracy and in a matter of minutes. The solution outperformed manual current practices, which have an 80% accuracy rate. 

It was possible to gradually capture and transfer the expertise of the subject matter experts from human to machine with annotated pictures. Additionally, map visualization allowed correlations between the rust severity level and the nature of the surrounding environment. 

The use of Grid Vision resulted in a drastic reduction of time spent reviewing inspection data and in a better detection accuracy. This represents a huge opportunity to rapidly improve the inspection process and reduce costs.​ Energinet will be able to record rust progression over time, which will enable predictive maintenance and bring future value.



Want to achieve similar results?

Our customer success team ensures that our software has maximum impact and implementation across our customer base. For this reason, we have designed a 3-step customer engagement journey where we work together to understand your needs and design a customized solution for you.



1-2 days

Provides insight about our software solution and capabilities. Together, we assess your current business processes and agree on use case(s) and prioritize for maximum business impact. Next, we define criteria for converting pilot to SaaS.


2-4 months

Develop and demonstrate agreed upon use case(s). Quantify value and evaluate the solution.



Implementing the product and forming a long-term partnership. Defining and agreeing upon new use cases to ensure maximum business impact.

About the customer

Energinet is an independent public enterprise owned by the Danish Ministry of Climate and Energy. Energinet owns, operates, and develops transmission systems for electricity and natural gas in Denmark. 

Energinet’s social mission is to convert older energy systems in order to ensure that citizens and businesses use renewable energy for everything. All with a stable supply and at an affordable price

Take the next step 

Speak with one our experts to learn more about our product