Applied Sciences (Nov 2021)

Automated Ground Truth Generation for Learning-Based Crack Detection on Concrete Surfaces

  • Hsiang-Chieh Chen,
  • Zheng-Ting Li

DOI
https://doi.org/10.3390/app112210966
Journal volume & issue
Vol. 11, no. 22
p. 10966

Abstract

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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.

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