KrakN: Transfer Learning framework for thin crack detection in infrastructure maintenance
This addresses the need for efficient and accessible crack detection in infrastructure management, particularly for government units with limited resources, though it appears incremental in applying transfer learning to a specific domain.
The paper tackles the problem of detecting thin cracks in infrastructure maintenance by proposing KrakN, a transfer learning framework that achieves over 90% accuracy while reducing dataset creation effort and computing power requirements.
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 Currently applied methods are outdated, labour-intensive and inaccurate.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.