Deep Transfer Learning Approach for Identifying Slope Surface Cracks

Abstract

Geohazards such as landslides, which are often accompanied by surface cracks, have caused great harm to public safety and property. If these surface cracks could be identified in time, this would be of great significance for the monitoring and early warning of geohazards. Currently, the most common method for crack identification is manual detection, which has low efficiency and accuracy. In this paper, a deep transfer learning approach 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) a large sample dataset of concrete cracks and (b) a small sample dataset of soil and rock masses’ cracks. In the proposed approach, (1) pretrained crack identification models are constructed based on a large sample dataset of concrete cracks; (2) refined crack identification models are further constructed based on a small sample dataset of soil and rock masses’ cracks. The proposed approach could be applied to conduct UAV surveys on high and steep slopes to provide monitoring and early warning of landslides to ensure the safety of people and property.

Keywords

• geological disasters
• landslide
• slope surface crack
• transfer learning
• deep learning