Papers › KrakN: Transfer Learning framework for thin crack detection in infrastructure maintenance
KrakN: Transfer Learning framework for thin crack detection in infrastructure maintenance
Mateusz Żarski, Bartosz Wójcik, Jarosław Adam Miszczak
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 Currently applied methods are outdated, labour-intensive and inaccurate.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.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections