# Deep Transfer Learning for Identifications of Slope Surface Cracks

arXiv: https://arxiv.org/abs/2108.04235 · HTML: https://mlsanity.com/paper/2108.04235/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 42 references that are in the map.
- First posted on arXiv 8 Aug 2021.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Road Damage Detection Using Deep Neural Networks with Images Captured Through a Smartphone](https://mlsanity.com/paper/1801.09454/) (2018)
- [Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding](https://mlsanity.com/paper/1904.08582/) (2019)

## Abstract (arXiv)

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.