Buy article PDF
The purchased file will be sent to you
via email after the payment is completed.
US$ 35
|
Smart Structures and Systems Volume 29, Number 1, January 2022 , pages 195-206 DOI: https://doi.org/10.12989/sss.2022.29.1.195 |
|
|
|
An active learning method with difficulty learning mechanism for crack detection |
||
Jiangpeng Shu, Jun Li, Jiawei Zhang, Weijian Zhao, Yuanfeng Duan and Zhicheng Zhang
|
||
| Abstract | ||
| Crack detection is essential for inspection of existing structures and crack segmentation based on deep learning is asignificant solution. However, datasets are usually one of the key issues. When building a new dataset for deep learning, laborious and time-consuming annotation of a large number of crack images is an obstacle. The aim of this study is to develop an approach that can automatically select a small portion of the most informative crack images from a large pool in order to annotate them, not to label all crack images. An active learning method with difficulty learning mechanism for crack segmentation tasks is proposed. Experiments are carried out on a crack image dataset of a steel box girder, which contains 500 images of 320 | ||
| Key Words | ||
| acquisition function; active learning; crack detection; probability attention module; semantic segmentation | ||
| Address | ||
| (1) Jiangpeng Shu, Jun Li, Jiawei Zhang, Weijian Zhao, Yuanfeng Duan, Zhicheng Zhang: College of Civil Engineering and Architecture, Zhejiang University, 310058 Hangzhou, China; (2) Jun Li: Center for Balance Architecture, Zhejiang University, 310058 Hangzhou, China. | ||