  Monitoring the technical condition of infrastructure is a crucial element to
its maintenance. Currently applied methods are outdated, labour-intensive and
inaccurate. At the same time, the latest methods using Artificial Intelligence
techniques are severely limited in their application due to two main factors --
labour-intensive gathering of new datasets and high demand for computing power.
We propose to utilize custom made framework -- KrakN, to overcome these
limiting factors. It enables the development of unique infrastructure defects
detectors on digital images, achieving the accuracy of above 90%. The framework
supports semi-automatic creation of new datasets and has modest computing power
requirements. It is implemented in the form of a ready-to-use software package
openly distributed to the public. Thus, it can be used to immediately implement
the methods proposed in this paper in the process of infrastructure management
by government units, regardless of their financial capabilities.
