Crack Detection Method for Tunnel Lining Surfaces using Ternary Classifier
- Authors
- Han, Jeong Hoon; Kim, In Soo; Lee, Cheol Hee; Moon, Young Shik
- Issue Date
- Sep-2020
- Publisher
- 한국인터넷정보학회
- Keywords
- Crack Detection; Convolutional Neural Network; Tunnel Lining Inspection
- Citation
- KSII Transactions on Internet and Information Systems, v.14, no.9, pp.3797 - 3822
- Indexed
- SCIE
SCOPUS
KCI
- Journal Title
- KSII Transactions on Internet and Information Systems
- Volume
- 14
- Number
- 9
- Start Page
- 3797
- End Page
- 3822
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/860
- DOI
- 10.3837/tiis.2020.09.013
- ISSN
- 1976-7277
- 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.
- Files in This Item
-
Go to Link
- Appears in
Collections - COLLEGE OF COMPUTING > SCHOOL OF COMPUTER SCIENCE > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/860)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.