Deep Transfer Learning for Identifications of Slope Surface Cracks. (arXiv:2108.04235v1 [cs.CV])

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.

Source: https://arxiv.org/abs/2108.04235

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