Abstract
This study aims to discuss vibration extraction of a pole structure from a video processing as a preliminary investigation utilizing video footage from an inspection vehicle. The proposed method transforms the video footage into frequency domain, magnifies the featured frequency bands and then reconstructs it. The vibration patterns of the target pole is extracted from the magnified video, and vibration characteristics are estimated by a statistical approach. To investigate the feasibility of the proposed method, a full-scale laboratory experiment for a pole structure is conducted. Vibration characteristics from the video footage are compared with those identified from accelerometers to investigated identification accuracy using the video footage.
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References
Gonzalez, A., O’Brien, E.J., Li, Y.Y.: The use of vehicle acceleration measurements to estimate road roughness. Veh. Syst. Dyn. 46(6), 483–499 (2008)
Hasegawa, S., Kim, C.W., Toshi, N., Chang, K.C.: Inverse analysis for road roughness profile identification utilizing acceleration of a moving vehicle. In: EVACES 2021, 17–20 September 2021, Tokyo, Japan (2021). (Book: Lecture Notes in Civil Engineering, vol. 224, Wu, Z., et al. (eds.) Experimental Vibration Analysis for Civil Engineering Structures)
Kim, C.W., Kawatani, M.: Challenge for a drive-by bridge inspection. In: Safety Reliability and Risk of Structures Infrastructures and Engineering Systems, pp. 758–765 (2009)
Luca, P., Tomas, M., Gonzalo, F., Stian, B.: Corrosion detection using AI: a comparison of standard computer vision techniques and deep learning model. In: The Sixth International Conference on Computer Science, Engineering and Information Technology, pp. 91–99 (2016)
Kim, C.W., Chang, K.C., Sasaka, Y., Suzuki, Y.: A feasibility study on crack identification utilizing images taken from camera mounted on a mobile robot. Proc. Eng. 188, 48–55 (2017)
Doebling, S.W.S., Farrar, C.R., Prime, M.B., Shevitz, D.W.: Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: a literature review. Los Alamos National Laboratory Report LA-13070-MS (1996)
Chu, T.C., Ranson, W.F., Sutton, M.A., Peters, W.H.: Applications of digital-image-correlation techniques to experimental mechanics. Exp. Mech. 25(3), 232–244 (1985)
Wadhwa, N., Rubinstein, M. Durand, F., Freeman, W. T.: Phase-based motion processing. ACM Trans. Graph. 32(4), 80_1–10 (2013)
Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8(6), 679–698 (1986)
Wu, H., Rubinstein, M., Shih, E., Guttag, J., Durand, F., Freeman, W.: Eulerian video magnification for revealing subtle changes in the world. ACM Trans. Graph. 31(4), 1–8 (2012)
Boomgard, V.D.R., van Balen, R.: Methods for fast morphological image transforms using bitmapped images. Comput. Vis. Graph. Image Process.: Graph. Models Image Process. 54(3), 254–258 (1992)
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This work was supported by JSPS KAKENHI Grant Number JP21K20448.
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Kawabe, D., Kim, CW. (2023). Fundamental Study on Extracting Vibration of Pole Structure from Vehicle Footage. In: Rizzo, P., Milazzo, A. (eds) European Workshop on Structural Health Monitoring. EWSHM 2022. Lecture Notes in Civil Engineering, vol 253. Springer, Cham. https://doi.org/10.1007/978-3-031-07254-3_69
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DOI: https://doi.org/10.1007/978-3-031-07254-3_69
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