Department of Industrial Engineering, Ajou University, Suwon, Republic of Korea
Email: leeky16498@ajou.ac.kr (K.Y.L.); mynamedaeil@ajou.ac.kr (D.I.J.); yk73@ajou.ac.kr (Y.J.K.)
*Corresponding author
Manuscript received January 1, 2024; revised March 1, 2024; accepted May 10, 2024; published May 28, 2024.
Abstract—As the development of various IoT devices are continuously expanding, the vision-based systems are gradually increasing in the field of Structural Health Monitoring (SHM) system. This paper focuses on the development of a computational algorithm to measure the frequency of structural beams from vision image data. The image processing algorithms are contained in a small IoT device, Raspberry Pi 4 and compatible HQ camera. The device analyzes the changes in the frequency in accordance with the variations in in the bolt looseness. For the accurate analysis, image processing techniques using OpenCV, Fourier transform, and mathematical model have been used. The result of the research shows the great potential for real-world applications, which is expected to save a lot of cost and effort.
Keywords—bolt looseness, vision-based structural health monitoring, cost-effectiveness, small IoT device, edge computer
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Cite: Kyungyun Lee, Daeil Jo, and Yongjin Kwon, "Vision-Based Structural Health Monitoring System Using Edge Computing Device," International Journal of Modeling and Optimization, vol. 14, no. 2, pp. 76-80, 2024.
Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
