Details

Autor(en) / Beteiligte

• Chen, Hsiang-Chieh
• Li, Zheng-Ting

Titel

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

Ist Teil von

• Applied sciences, 2021-11, Vol.11 (22), p.10966

Ort / Verlag

Basel: MDPI AG

Erscheinungsjahr

2021

Link zum Volltext

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• 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.