Deep Transfer Learning for Identifications of Slope Surface Cracks
This work addresses the need for timely crack detection in geohazard-prone areas to improve safety, but it is incremental as it applies existing transfer learning methods to a new domain-specific dataset.
The paper tackles the problem of identifying slope surface cracks for landslide monitoring by proposing a deep transfer learning framework that trains on large concrete crack datasets and refines with small soil/rock crack datasets, achieving effective and efficient identification to enable fast UAV surveys.
Geohazards such as landslides have caused great losses to the safety of people's lives and property, which is often accompanied with surface cracks. If such surface cracks could be identified in time, it is of great significance for the monitoring and early warning of geohazards. Currently, the most common method for crack identification is manual detection, which is with low efficiency and accuracy. In this paper, a deep transfer learning framework is proposed to effectively and efficiently identify slope surface cracks for the sake of fast monitoring and early warning of geohazards such as landslides. The essential idea is to employ transfer learning by training (a) the large sample dataset of concrete cracks and (b) the small sample dataset of soil and rock masses cracks. In the proposed framework, (1) pretrained cracks identification models are constructed based on the large sample dataset of concrete cracks; (2) refined cracks identification models are further constructed based on the small sample dataset of soil and rock masses cracks. The proposed framework could be applied to conduct UAV surveys on high-steep slopes to realize the monitoring and early warning of landslides to ensure the safety of people's lives and property.