Abstract
This article introduces an automated data-labeling approach for generating crack ground truths (GTs) within concrete images. The main algorithm includes generating first-round GTs, pre-training a deep learning-based model, and generating second-round GTs. On the basis of the generated second-round GTs of the training data, a learning-based crack detection model can be trained in a self-supervised manner. The pre-trained deep learning-based model is effective for crack detection after it is re-trained using the second-round GTs. The main contribution of this study is the proposal of an automated GT generation process for training a crack detection model at the pixel level. Experimental results show that the second-round GTs are similar to manually marked labels. Accordingly, the cost of implementing learning-based methods is reduced significantly because data labeling by humans is not necessitated.
Original language | English |
---|---|
Article number | 10966 |
Journal | Applied Sciences (Switzerland) |
Volume | 11 |
Issue number | 22 |
DOIs | |
State | Published - Nov 2021 |
Keywords
- Automated data labeling
- Crack detection
- Crack segmentation
- Deep learning
- Ground truth generation