Automatic Concrete Damage Recognition Using Multi-Level Attention Convolutional Neural Networkopen access
- Authors
- Shin, Hyun Kyu; Ahn, Yong Han; Lee, Sang Hyo; Kim, Ha Young
- Issue Date
- Dec-2020
- Publisher
- MDPI Open Access Publishing
- Keywords
- concrete defects; damage recognition; convolutional neural network; deep learning; attention network
- Citation
- Materials, v.13, no.23, pp 5549 - 5561
- Pages
- 13
- Indexed
- SCIE
SCOPUS
- Journal Title
- Materials
- Volume
- 13
- Number
- 23
- Start Page
- 5549
- End Page
- 5561
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/719
- DOI
- 10.3390/ma13235549
- ISSN
- 1996-1944
1996-1944
- Abstract
- There has been an increase in the deterioration of buildings and infrastructure in dense urban regions, and several defects in the structures are being exposed. To ensure the effective diagnosis of building conditions, vision-based automatic damage recognition techniques have been developed. However, conventional image processing techniques have some limitations in real-world situations owing to their manual feature extraction approach. To overcome these limitations, a convolutional neural network-based image recognition technique was adopted in this study, and a convolution-based concrete multi-damage recognition neural network (CMDnet) was developed. The image datasets consisted of 1981 types of concrete surface damages, including surface cracks, rebar exposure and delamination, as well as intact. Furthermore, it was experimentally demonstrated that the proposed model could accurately classify the damage types. The results obtained in this study reveal that the proposed model can recognize the different damage types from digital images of the surfaces of concrete structures. The trained CMDnet demonstrated a damage-detection accuracy of 98.9%. Moreover, the proposed model could be applied in automatic damage detection networks to achieve superior performance with regard to concrete surface damage detection and recognition, as well as accelerating efficient damage identification during the diagnosis of deteriorating structures used in civil engineering applications.
- Files in This Item
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- Appears in
Collections - COLLEGE OF ENGINEERING SCIENCES > MAJOR IN BUILDING INFORMATION TECHNOLOGY > 1. Journal Articles
- COLLEGE OF ENGINEERING SCIENCES > MAJOR IN ARCHITECTURAL ENGINEERING > 1. Journal Articles
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