Damage detection on physical assets

product info

by combining machine learning algorithms and remote inspection, we scan through terabytes of data to make sure that you have complete control over the health of your physical assets.


Automatic Inspection

We use unsupervised, supervised, and generative algorithms to automatically scan through billions of images.

Manual Inspection

Our digital inspectors manually remove false positives so that you can focus on the actual damages.


By continuously annotating images and retraining the algorithms, the solution becomes almost automatic with only a tiny amount of expert supervision.

Integration & Reporting

We integrate our software into your systems to run algorithms in real–time and report damages directly to your asset management system.


Our algorithm can find damages that are nearly invisible to humans. We have generative networks that create fake damages for additional training data.

We're analyzing billions of images and have found hundreds of damages.


  • automatic inventory of your assets
  • analyze asset breakdown over time
  • find damages on infrastructure
  • find damage during manufacturing
  • save thousands of hours by automatic inspection

customer cases

Current customer cases for DeepInspection

Detecting Damage on Catenary Wires

A broken catenary wire can stop the train traffic for hours and cost millions. With Infranord and their measurement train, we use DeepInspection to scan 20 000 km of railway in Sweden. In 2021, we analyzed over 500 M images and found more than 150 damages.

Identifying Cracks in Sleepers

Small cracks in concrete sleepers can soon evolve and cause dislocation of sleepers and the rail. In extreme cases, it can lead to derailments. To avoid this outcome, we use DeepInspection to detect and classify cracks early in their development to make sure cracked sleepers are changed.