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Non-contact vibration sensor using deep learning and image processing
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DOI：10.1016/j.measurement.2021.109823.png)
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Abstract
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This paper proposes a non-contact vibration measurement method based on deep learning and image processing. The deep learning method is used to realize the automatic and efficient selection of effective pixels and the optical flow method is used to extract vibration signals to realize non-contact and targetless visual vibration measurement. In this study, a carbon plate board and aluminum C-beam structure were measured and verified under artificial and non-human excitation in a laboratory environment. Additionally, bridge and cable structures in an outdoor environment were selected as measurement targets to verify the reliability of the proposed method. This paper compares the experimental results of Canny and Sobel edge detection algorithms and deep learning methods to verify the efficiency of deep learning. The results demonstrate that our method is robust, even under real-world unfavorable conditions, meaning it can serve as a novel measurement method in the field of vibration measurement.
Keywords:
Vibration measurement
Optical flow
Deep learning
Convolutional neural network
Photogrammetry
Computer vision
Non-contact measurement
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