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A platform for research: civil engineering, architecture and urbanism
Automated defect detection in FRP-bonded structures by Eulerian video magnification and adaptive background mixture model
Abstract 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 de-bonding 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.
Highlights An automated defect detection of FRP-bonded civil structure is demonstrated. The study aims at identifying the de-bonding defect between FRP and concrete. Eulerian video magnification with adaptive background mixture model is used. Eulerian video magnification reveals motion of de-bonding zone in high-speed video. Adaptive background mixture model can automatically track the de-bonding motion.
Automated defect detection in FRP-bonded structures by Eulerian video magnification and adaptive background mixture model
Abstract 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 de-bonding 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.
Highlights An automated defect detection of FRP-bonded civil structure is demonstrated. The study aims at identifying the de-bonding defect between FRP and concrete. Eulerian video magnification with adaptive background mixture model is used. Eulerian video magnification reveals motion of de-bonding zone in high-speed video. Adaptive background mixture model can automatically track the de-bonding motion.
Automated defect detection in FRP-bonded structures by Eulerian video magnification and adaptive background mixture model
Qiu, Qiwen (author)
2020-04-22
Article (Journal)
Electronic Resource
English
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