基于计算机视觉和深度学习的隔震支座动态位移测量方法

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Abstract Aiming at the visual measurement characteristics of the isolation bearing during an earthquake, that is, both the camera and the isolation bearing will move during an earthquake, and it is inconvenient to track the camera pose. This paper proposes a dynamic displacement measurement method of the isolation bearing without additional tracking of the camera movement. Firstly, the camera and the target are set in the orthogonal horizontal directions respectively, and the optical axis of the camera and the target plane are perpendicular to each other, and the scale factor method is used to calibrate the camera in each direction and time; Secondly, using the deep learning method, a YOLOv5 model is trained to automatically identify and locate the target, and the prediction box of YOLOv5 is enlarged and the target is repositioned by performing feature recognition and extraction on the enlarged image. Finally, the relative displacement of the target at the upper and lower connecting plates of the bearing is used to determine the dynamic displacement of the isolation bearing. The method was verified by photographing the mechanical performance test process of LRB500 isolation bearing. The results show that the error between the horizontal displacement time-history curve of the bearing obtained by this method and the displacement meter results at each moment is less than 1.0%, the absolute error of the horizontal displacement peak value is 1.042mm, and the absolute error of the vertical displacement peak value is 0.219mm. It shows that the method proposed in this paper has high detection accuracy, and can complete the deformation detection of the isolation bearing under the action of earthquake without tracking the camera pose.

Key words： deep learning computer vision isolation bearing dynamic displacement YOLOv5;

Received: 16 February 2022 Published: 28 March 2023

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	DANG Yu
	HE Yizhe

Cite this article:

DANG Yu,HE Yizhe. Dynamic displacement measurement method for an isolation bearing based on computer vision and deep learning[J]. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(6): 90-97.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2023/V42/I6/90

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