TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Crack Detection Method for Tunnel Lining Surfaces using Ternary Classifier |
| 영문제목(English Title) |
Crack Detection Method for Tunnel Lining Surfaces using Ternary Classifier |
| 저자(Author) |
Jeong Hoon Han
In Soo Kim
Cheol Hee Lee
Young Shik Moon
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| 원문수록처(Citation) |
VOL 14 NO. 09 PP. 3797 ~ 3822 (2020. 09) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
The inspection of cracks on the surface of tunnel linings is a common method of evaluate the condition of the tunnel. In particular, determining the thickness and shape of a crack is important because it indicates the external forces applied to the tunnel and the current condition of the concrete structure. Recently, several automatic crack detection methods have been proposed to identify cracks using captured tunnel lining images. These methods apply an image-segmentation mechanism with well-annotated datasets. However, generating the ground truths requires many resources, and the small proportion of cracks in the images cause a class-imbalance problem. A weakly annotated dataset is generated to reduce resource consumption and avoid the class-imbalance problem. However, the use of the dataset results in a large number of false positives and requires post-processing for accurate crack detection. To overcome these issues, we propose a crack detection method using a ternary classifier. The proposed method significantly reduces the false positive rate, and the performance (as measured by the F1 score) is improved by 0.33 compared to previous methods. These results demonstrate the effectiveness of the proposed method.
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| 키워드(Keyword) |
Crack Detection
Convolutional Neural Network
Tunnel Lining Inspection
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| 파일첨부 |
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