Automated Ground Truth Generation for Learning-Based Crack Detection on Concrete Surfaces
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 G...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/22/10966 |
| _version_ | 1797511322339901440 |
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| author | Hsiang-Chieh Chen Zheng-Ting Li |
| author_facet | Hsiang-Chieh Chen Zheng-Ting Li |
| author_sort | Hsiang-Chieh Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T05:43:43Z |
| format | Article |
| id | doaj.art-ebcf0a49da7240aa9738c10a4bfdbc2c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T05:43:43Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-ebcf0a49da7240aa9738c10a4bfdbc2c2023-11-22T22:21:38ZengMDPI AGApplied Sciences2076-34172021-11-0111221096610.3390/app112210966Automated Ground Truth Generation for Learning-Based Crack Detection on Concrete SurfacesHsiang-Chieh Chen0Zheng-Ting Li1Department of Electrical Engineering, National United University, Miaoli 360301, TaiwanDepartment of Electrical Engineering, National United University, Miaoli 360301, TaiwanThis 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.https://www.mdpi.com/2076-3417/11/22/10966automated data labelingcrack detectioncrack segmentationdeep learningground truth generation |
| spellingShingle | Hsiang-Chieh Chen Zheng-Ting Li Automated Ground Truth Generation for Learning-Based Crack Detection on Concrete Surfaces Applied Sciences automated data labeling crack detection crack segmentation deep learning ground truth generation |
| title | Automated Ground Truth Generation for Learning-Based Crack Detection on Concrete Surfaces |
| title_full | Automated Ground Truth Generation for Learning-Based Crack Detection on Concrete Surfaces |
| title_fullStr | Automated Ground Truth Generation for Learning-Based Crack Detection on Concrete Surfaces |
| title_full_unstemmed | Automated Ground Truth Generation for Learning-Based Crack Detection on Concrete Surfaces |
| title_short | Automated Ground Truth Generation for Learning-Based Crack Detection on Concrete Surfaces |
| title_sort | automated ground truth generation for learning based crack detection on concrete surfaces |
| topic | automated data labeling crack detection crack segmentation deep learning ground truth generation |
| url | https://www.mdpi.com/2076-3417/11/22/10966 |
| work_keys_str_mv | AT hsiangchiehchen automatedgroundtruthgenerationforlearningbasedcrackdetectiononconcretesurfaces AT zhengtingli automatedgroundtruthgenerationforlearningbasedcrackdetectiononconcretesurfaces |