Automated ground truth generation for learning-based crack detection on concrete surfaces

Hsiang Chieh Chen, Zheng Ting Li

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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 languageEnglish
Article number10966
JournalApplied Sciences (Switzerland)
Issue number22
StatePublished - Nov 2021


  • Automated data labeling
  • Crack detection
  • Crack segmentation
  • Deep learning
  • Ground truth generation


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