期刊
AUTOMATION IN CONSTRUCTION
卷 116, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.autcon.2020.103244
关键词
Defect detection; Civil engineering structure; Fiber-reinforced polymer; Computer-aided engineering; Automation; Motion magnification; High-speed video; Computer vision; Information technology; Non-destructive testing
资金
- Shenzhen University
- City University of Hong Kong
- Hong Kong Polytechnic University
A computer-aided methodology for automated defect detection in fiber-reinforced polymer (FRP) bonded civil engineering structures via Eulerian video magnification integrated with adaptive background mixture model is demonstrated. In this methodology, Eulerian video magnification is firstly used to render the stimulated debonding motion visible in a high-speed and high-resolution video. Then, adaptive background mixture model is applied in the motion-magnified video for automated tracking of the de-bonding motion. The combined use of these two computer-aided vision techniques aims at developing an innovative way for intuitive, straightforward, and automated defect detection. In the application of this methodology, two operational parameters (i.e. motion amplification factor and de-noising factor) in the video processing can greatly affect the sensitivity of defect detection. After evaluation of their effects, the present work shows a practice guide for adjusting the above two influencing parameters in order to improve the automated defect detection.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据